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MTUT (CVPR’2019)


Mtut + I3d on Nvgesture

MTUT (CVPR'2019)
@InProceedings{Abavisani_2019_CVPR,
  author = {Abavisani, Mahdi and Joze, Hamid Reza Vaezi and Patel, Vishal M.},
  title = {Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition With Multimodal Training},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2019}
}
I3D (CVPR'2017)
@InProceedings{Carreira_2017_CVPR,
  author = {Carreira, Joao and Zisserman, Andrew},
  title = {Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {July},
  year = {2017}
}
NVGesture (CVPR'2016)
@InProceedings{Molchanov_2016_CVPR,
  author = {Molchanov, Pavlo and Yang, Xiaodong and Gupta, Shalini and Kim, Kihwan and Tyree, Stephen and Kautz, Jan},
  title = {Online Detection and Classification of Dynamic Hand Gestures With Recurrent 3D Convolutional Neural Network},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2016}
}

Results on NVGesture test set

Arch Input Size fps bbox AP_rgb AP_depth ckpt log
I3D+MTUT* 112x112 15 $\surd$ 0.725 0.730 ckpt log
I3D+MTUT 224x224 30 $\surd$ 0.782 0.811 ckpt log
I3D+MTUT 224x224 30 $\times$ 0.739 0.809 ckpt log

*: MTUT supports multi-modal training and uni-modal testing. Model trained with this config can be used to recognize gestures in rgb videos with inference config.




MSPN (ArXiv’2019)


Topdown Heatmap + MSPN on Coco

MSPN (ArXiv'2019)
@article{li2019rethinking,
  title={Rethinking on Multi-Stage Networks for Human Pose Estimation},
  author={Li, Wenbo and Wang, Zhicheng and Yin, Binyi and Peng, Qixiang and Du, Yuming and Xiao, Tianzi and Yu, Gang and Lu, Hongtao and Wei, Yichen and Sun, Jian},
  journal={arXiv preprint arXiv:1901.00148},
  year={2019}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
mspn_50 256x192 0.723 0.895 0.794 0.788 0.933 ckpt log
2xmspn_50 256x192 0.754 0.903 0.825 0.815 0.941 ckpt log
3xmspn_50 256x192 0.758 0.904 0.830 0.821 0.943 ckpt log
4xmspn_50 256x192 0.764 0.906 0.835 0.826 0.944 ckpt log



InterNet (ECCV’2020)


Internet + Internet on Interhand3d

InterNet (ECCV'2020)
@InProceedings{Moon_2020_ECCV_InterHand2.6M,
author = {Moon, Gyeongsik and Yu, Shoou-I and Wen, He and Shiratori, Takaaki and Lee, Kyoung Mu},
title = {InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2020}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
InterHand2.6M (ECCV'2020)
@InProceedings{Moon_2020_ECCV_InterHand2.6M,
author = {Moon, Gyeongsik and Yu, Shoou-I and Wen, He and Shiratori, Takaaki and Lee, Kyoung Mu},
title = {InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2020}
}

Results on InterHand2.6M val & test set

Train Set Set Arch Input Size MPJPE-single MPJPE-interacting MPJPE-all MRRPE APh ckpt log
All test(H+M) InterNet_resnet_50 256x256 9.47 13.40 11.59 29.28 0.99 ckpt log
All val(M) InterNet_resnet_50 256x256 11.22 15.23 13.16 31.73 0.98 ckpt log



DEKR (CVPR’2021)


Dekr + Hrnet on Coco

DEKR (CVPR'2021)
@inproceedings{geng2021bottom,
  title={Bottom-up human pose estimation via disentangled keypoint regression},
  author={Geng, Zigang and Sun, Ke and Xiao, Bin and Zhang, Zhaoxiang and Wang, Jingdong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14676--14686},
  year={2021}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32 512x512 0.680 0.868 0.745 0.728 0.897 ckpt log
HRNet-w48 640x640 0.709 0.876 0.773 0.758 0.909 ckpt log

Results on COCO val2017 with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt
HRNet-w32* 512x512 0.705 0.878 0.767 0.759 0.921 ckpt
HRNet-w48* 640x640 0.722 0.882 0.785 0.778 0.928 ckpt

* these configs are generally used for evaluation. The training settings are identical to their single-scale counterparts.

The results of models provided by the authors on COCO val2017 using the same evaluation protocol

Arch Input Size Setting AP AP50 AP75 AR AR50 ckpt
HRNet-w32 512x512 single-scale 0.678 0.868 0.744 0.728 0.897 see official implementation
HRNet-w48 640x640 single-scale 0.707 0.876 0.773 0.757 0.909 see official implementation
HRNet-w32 512x512 multi-scale 0.708 0.880 0.773 0.763 0.921 see official implementation
HRNet-w48 640x640 multi-scale 0.721 0.881 0.786 0.779 0.927 see official implementation

The discrepancy between these results and that shown in paper is attributed to the differences in implementation details in evaluation process.


Dekr + Hrnet on Crowdpose

DEKR (CVPR'2021)
@inproceedings{geng2021bottom,
  title={Bottom-up human pose estimation via disentangled keypoint regression},
  author={Geng, Zigang and Sun, Ke and Xiao, Bin and Zhang, Zhaoxiang and Wang, Jingdong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14676--14686},
  year={2021}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
CrowdPose (CVPR'2019)
@article{li2018crowdpose,
  title={CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark},
  author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},
  journal={arXiv preprint arXiv:1812.00324},
  year={2018}
}

Results on CrowdPose test without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32 512x512 0.663 0.857 0.715 0.719 0.893 ckpt log
HRNet-w48 640x640 0.682 0.869 0.736 0.742 0.911 ckpt log

Results on CrowdPose test with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt
HRNet-w32* 512x512 0.692 0.874 0.748 0.755 0.926 ckpt
HRNet-w48* 640x640 0.696 0.869 0.749 0.769 0.933 ckpt

* these configs are generally used for evaluation. The training settings are identical to their single-scale counterparts.




HigherHRNet (CVPR’2020)


Associative Embedding + Higherhrnet on Aic

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HigherHRNet (CVPR'2020)
@inproceedings{cheng2020higherhrnet,
  title={HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation},
  author={Cheng, Bowen and Xiao, Bin and Wang, Jingdong and Shi, Honghui and Huang, Thomas S and Zhang, Lei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5386--5395},
  year={2020}
}
AI Challenger (ArXiv'2017)
@article{wu2017ai,
  title={Ai challenger: A large-scale dataset for going deeper in image understanding},
  author={Wu, Jiahong and Zheng, He and Zhao, Bo and Li, Yixin and Yan, Baoming and Liang, Rui and Wang, Wenjia and Zhou, Shipei and Lin, Guosen and Fu, Yanwei and others},
  journal={arXiv preprint arXiv:1711.06475},
  year={2017}
}

Results on AIC validation set without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HigherHRNet-w32 512x512 0.315 0.710 0.243 0.379 0.757 ckpt log

Results on AIC validation set with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HigherHRNet-w32 512x512 0.323 0.718 0.254 0.379 0.758 ckpt log

Associative Embedding + Higherhrnet + Udp on Coco

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HigherHRNet (CVPR'2020)
@inproceedings{cheng2020higherhrnet,
  title={HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation},
  author={Cheng, Bowen and Xiao, Bin and Wang, Jingdong and Shi, Honghui and Huang, Thomas S and Zhang, Lei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5386--5395},
  year={2020}
}
UDP (CVPR'2020)
@InProceedings{Huang_2020_CVPR,
  author = {Huang, Junjie and Zhu, Zheng and Guo, Feng and Huang, Guan},
  title = {The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HigherHRNet-w32_udp 512x512 0.678 0.862 0.736 0.724 0.890 ckpt log
HigherHRNet-w48_udp 512x512 0.690 0.872 0.750 0.734 0.891 ckpt log

Associative Embedding + Higherhrnet on Coco

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HigherHRNet (CVPR'2020)
@inproceedings{cheng2020higherhrnet,
  title={HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation},
  author={Cheng, Bowen and Xiao, Bin and Wang, Jingdong and Shi, Honghui and Huang, Thomas S and Zhang, Lei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5386--5395},
  year={2020}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HigherHRNet-w32 512x512 0.677 0.870 0.738 0.723 0.890 ckpt log
HigherHRNet-w32 640x640 0.686 0.871 0.747 0.733 0.898 ckpt log
HigherHRNet-w48 512x512 0.686 0.873 0.741 0.731 0.892 ckpt log

Results on COCO val2017 with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HigherHRNet-w32 512x512 0.706 0.881 0.771 0.747 0.901 ckpt log
HigherHRNet-w32 640x640 0.706 0.880 0.770 0.749 0.902 ckpt log
HigherHRNet-w48 512x512 0.716 0.884 0.775 0.755 0.901 ckpt log

Associative Embedding + Higherhrnet on Crowdpose

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HigherHRNet (CVPR'2020)
@inproceedings{cheng2020higherhrnet,
  title={HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation},
  author={Cheng, Bowen and Xiao, Bin and Wang, Jingdong and Shi, Honghui and Huang, Thomas S and Zhang, Lei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5386--5395},
  year={2020}
}
CrowdPose (CVPR'2019)
@article{li2018crowdpose,
  title={CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark},
  author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},
  journal={arXiv preprint arXiv:1812.00324},
  year={2018}
}

Results on CrowdPose test without multi-scale test

Arch Input Size AP AP50 AP75 AP (E) AP (M) AP (H) ckpt log
HigherHRNet-w32 512x512 0.655 0.859 0.705 0.728 0.660 0.577 ckpt log

Results on CrowdPose test with multi-scale test. 2 scales ([2, 1]) are used

Arch Input Size AP AP50 AP75 AP (E) AP (M) AP (H) ckpt log
HigherHRNet-w32 512x512 0.661 0.864 0.710 0.742 0.670 0.566 ckpt log

Associative Embedding + Higherhrnet on Coco-Wholebody

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HigherHRNet (CVPR'2020)
@inproceedings{cheng2020higherhrnet,
  title={HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation},
  author={Cheng, Bowen and Xiao, Bin and Wang, Jingdong and Shi, Honghui and Huang, Thomas S and Zhang, Lei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5386--5395},
  year={2020}
}
COCO-WholeBody (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody v1.0 val without multi-scale test

Arch Input Size Body AP Body AR Foot AP Foot AR Face AP Face AR Hand AP Hand AR Whole AP Whole AR ckpt log
HigherHRNet-w32+ 512x512 0.590 0.672 0.185 0.335 0.676 0.721 0.212 0.298 0.401 0.493 ckpt log
HigherHRNet-w48+ 512x512 0.630 0.706 0.440 0.573 0.730 0.777 0.389 0.477 0.487 0.574 ckpt log

Note: + means the model is first pre-trained on original COCO dataset, and then fine-tuned on COCO-WholeBody dataset. We find this will lead to better performance.




DeepPose (CVPR’2014)


Deeppose + Resnet on Coco

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
deeppose_resnet_50 256x192 0.526 0.816 0.586 0.638 0.887 ckpt log
deeppose_resnet_101 256x192 0.560 0.832 0.628 0.668 0.900 ckpt log
deeppose_resnet_152 256x192 0.583 0.843 0.659 0.686 0.907 ckpt log

Deeppose + Resnet + Rle on Coco

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
RLE (ICCV'2021)
@inproceedings{li2021human,
  title={Human pose regression with residual log-likelihood estimation},
  author={Li, Jiefeng and Bian, Siyuan and Zeng, Ailing and Wang, Can and Pang, Bo and Liu, Wentao and Lu, Cewu},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={11025--11034},
  year={2021}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
deeppose_resnet_50_rle 256x192 0.704 0.883 0.777 0.751 0.920 ckpt log
deeppose_resnet_101_rle 256x192 0.722 0.894 0.794 0.768 0.930 ckpt log
deeppose_resnet_152_rle 256x192 0.731 0.897 0.805 0.777 0.933 ckpt log
deeppose_resnet_152_rle 384x288 0.749 0.901 0.815 0.793 0.935 ckpt log

Deeppose + Resnet on Mpii

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
MPII (CVPR'2014)
@inproceedings{andriluka14cvpr,
  author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt},
  title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2014},
  month = {June}
}

Results on MPII val set

Arch Input Size Mean Mean@0.1 ckpt log
deeppose_resnet_50 256x256 0.825 0.174 ckpt log
deeppose_resnet_101 256x256 0.841 0.193 ckpt log
deeppose_resnet_152 256x256 0.850 0.198 ckpt log

Deeppose + Resnet + Rle on Mpii

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
RLE (ICCV'2021)
@inproceedings{li2021human,
  title={Human pose regression with residual log-likelihood estimation},
  author={Li, Jiefeng and Bian, Siyuan and Zeng, Ailing and Wang, Can and Pang, Bo and Liu, Wentao and Lu, Cewu},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={11025--11034},
  year={2021}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
MPII (CVPR'2014)
@inproceedings{andriluka14cvpr,
  author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt},
  title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2014},
  month = {June}
}

Results on MPII val set

Arch Input Size Mean Mean@0.1 ckpt log
deeppose_resnet_50_rle 256x256 0.860 0.263 ckpt log

Deeppose + Resnet on WFLW

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
WFLW (CVPR'2018)
@inproceedings{wu2018look,
  title={Look at boundary: A boundary-aware face alignment algorithm},
  author={Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, Quan and Cai, Yici and Zhou, Qiang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2129--2138},
  year={2018}
}

Results on WFLW dataset

The model is trained on WFLW train.

Arch Input Size NMEtest NMEpose NMEillumination NMEocclusion NMEblur NMEmakeup NMEexpression ckpt log
deeppose_res50 256x256 4.85 8.50 4.81 5.69 5.45 4.82 5.20 ckpt log

Deeppose + Resnet + Softwingloss on WFLW

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
SoftWingloss (TIP'2021)
@article{lin2021structure,
  title={Structure-Coherent Deep Feature Learning for Robust Face Alignment},
  author={Lin, Chunze and Zhu, Beier and Wang, Quan and Liao, Renjie and Qian, Chen and Lu, Jiwen and Zhou, Jie},
  journal={IEEE Transactions on Image Processing},
  year={2021},
  publisher={IEEE}
}
WFLW (CVPR'2018)
@inproceedings{wu2018look,
  title={Look at boundary: A boundary-aware face alignment algorithm},
  author={Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, Quan and Cai, Yici and Zhou, Qiang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2129--2138},
  year={2018}
}

Results on WFLW dataset

The model is trained on WFLW train.

Arch Input Size NMEtest NMEpose NMEillumination NMEocclusion NMEblur NMEmakeup NMEexpression ckpt log
deeppose_res50_softwingloss 256x256 4.41 7.77 4.37 5.27 5.01 4.36 4.70 ckpt log

Deeppose + Resnet + Wingloss on WFLW

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
Wingloss (CVPR'2018)
@inproceedings{feng2018wing,
  title={Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks},
  author={Feng, Zhen-Hua and Kittler, Josef and Awais, Muhammad and Huber, Patrik and Wu, Xiao-Jun},
  booktitle={Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on},
  year={2018},
  pages ={2235-2245},
  organization={IEEE}
}
WFLW (CVPR'2018)
@inproceedings{wu2018look,
  title={Look at boundary: A boundary-aware face alignment algorithm},
  author={Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, Quan and Cai, Yici and Zhou, Qiang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2129--2138},
  year={2018}
}

Results on WFLW dataset

The model is trained on WFLW train.

Arch Input Size NMEtest NMEpose NMEillumination NMEocclusion NMEblur NMEmakeup NMEexpression ckpt log
deeppose_res50_wingloss 256x256 4.64 8.25 4.59 5.56 5.26 4.59 5.07 ckpt log

Deeppose + Resnet on Deepfashion

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
DeepFashion (CVPR'2016)
@inproceedings{liuLQWTcvpr16DeepFashion,
 author = {Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou},
 title = {DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations},
 booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 month = {June},
 year = {2016}
}
DeepFashion (ECCV'2016)
@inproceedings{liuYLWTeccv16FashionLandmark,
 author = {Liu, Ziwei and Yan, Sijie and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
 title = {Fashion Landmark Detection in the Wild},
 booktitle = {European Conference on Computer Vision (ECCV)},
 month = {October},
 year = {2016}
 }

Results on DeepFashion val set

Set Arch Input Size PCK@0.2 AUC EPE ckpt log
upper deeppose_resnet_50 256x256 0.965 0.535 17.2 ckpt log
lower deeppose_resnet_50 256x256 0.971 0.678 11.8 ckpt log
full deeppose_resnet_50 256x256 0.983 0.602 14.0 ckpt log

Deeppose + Resnet on Onehand10k

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
OneHand10K (TCSVT'2019)
@article{wang2018mask,
  title={Mask-pose cascaded cnn for 2d hand pose estimation from single color image},
  author={Wang, Yangang and Peng, Cong and Liu, Yebin},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  volume={29},
  number={11},
  pages={3258--3268},
  year={2018},
  publisher={IEEE}
}

Results on OneHand10K val set

Arch Input Size PCK@0.2 AUC EPE ckpt log
deeppose_resnet_50 256x256 0.990 0.486 34.28 ckpt log

Deeppose + Resnet on Panoptic2d

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
CMU Panoptic HandDB (CVPR'2017)
@inproceedings{simon2017hand,
  title={Hand keypoint detection in single images using multiview bootstrapping},
  author={Simon, Tomas and Joo, Hanbyul and Matthews, Iain and Sheikh, Yaser},
  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
  pages={1145--1153},
  year={2017}
}

Results on CMU Panoptic (MPII+NZSL val set)

Arch Input Size PCKh@0.7 AUC EPE ckpt log
deeppose_resnet_50 256x256 0.999 0.686 9.36 ckpt log

Deeppose + Resnet on Rhd2d

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
RHD (ICCV'2017)
@TechReport{zb2017hand,
  author={Christian Zimmermann and Thomas Brox},
  title={Learning to Estimate 3D Hand Pose from Single RGB Images},
  institution={arXiv:1705.01389},
  year={2017},
  note="https://arxiv.org/abs/1705.01389",
  url="https://lmb.informatik.uni-freiburg.de/projects/hand3d/"
}

Results on RHD test set

Arch Input Size PCK@0.2 AUC EPE ckpt log
deeppose_resnet_50 256x256 0.988 0.865 3.29 ckpt log



RLE (ICCV’2021)


Deeppose + Resnet + Rle on Coco

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
RLE (ICCV'2021)
@inproceedings{li2021human,
  title={Human pose regression with residual log-likelihood estimation},
  author={Li, Jiefeng and Bian, Siyuan and Zeng, Ailing and Wang, Can and Pang, Bo and Liu, Wentao and Lu, Cewu},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={11025--11034},
  year={2021}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
deeppose_resnet_50_rle 256x192 0.704 0.883 0.777 0.751 0.920 ckpt log
deeppose_resnet_101_rle 256x192 0.722 0.894 0.794 0.768 0.930 ckpt log
deeppose_resnet_152_rle 256x192 0.731 0.897 0.805 0.777 0.933 ckpt log
deeppose_resnet_152_rle 384x288 0.749 0.901 0.815 0.793 0.935 ckpt log

Deeppose + Resnet + Rle on Mpii

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
RLE (ICCV'2021)
@inproceedings{li2021human,
  title={Human pose regression with residual log-likelihood estimation},
  author={Li, Jiefeng and Bian, Siyuan and Zeng, Ailing and Wang, Can and Pang, Bo and Liu, Wentao and Lu, Cewu},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={11025--11034},
  year={2021}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
MPII (CVPR'2014)
@inproceedings{andriluka14cvpr,
  author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt},
  title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2014},
  month = {June}
}

Results on MPII val set

Arch Input Size Mean Mean@0.1 ckpt log
deeppose_resnet_50_rle 256x256 0.860 0.263 ckpt log



SoftWingloss (TIP’2021)


Deeppose + Resnet + Softwingloss on WFLW

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
SoftWingloss (TIP'2021)
@article{lin2021structure,
  title={Structure-Coherent Deep Feature Learning for Robust Face Alignment},
  author={Lin, Chunze and Zhu, Beier and Wang, Quan and Liao, Renjie and Qian, Chen and Lu, Jiwen and Zhou, Jie},
  journal={IEEE Transactions on Image Processing},
  year={2021},
  publisher={IEEE}
}
WFLW (CVPR'2018)
@inproceedings{wu2018look,
  title={Look at boundary: A boundary-aware face alignment algorithm},
  author={Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, Quan and Cai, Yici and Zhou, Qiang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2129--2138},
  year={2018}
}

Results on WFLW dataset

The model is trained on WFLW train.

Arch Input Size NMEtest NMEpose NMEillumination NMEocclusion NMEblur NMEmakeup NMEexpression ckpt log
deeppose_res50_softwingloss 256x256 4.41 7.77 4.37 5.27 5.01 4.36 4.70 ckpt log



VideoPose3D (CVPR’2019)


Video Pose Lift + Videopose3d on H36m

VideoPose3D (CVPR'2019)
@inproceedings{pavllo20193d,
  title={3d human pose estimation in video with temporal convolutions and semi-supervised training},
  author={Pavllo, Dario and Feichtenhofer, Christoph and Grangier, David and Auli, Michael},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7753--7762},
  year={2019}
}
Human3.6M (TPAMI'2014)
@article{h36m_pami,
  author = {Ionescu, Catalin and Papava, Dragos and Olaru, Vlad and Sminchisescu,  Cristian},
  title = {Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  publisher = {IEEE Computer Society},
  volume = {36},
  number = {7},
  pages = {1325-1339},
  month = {jul},
  year = {2014}
}

Results on Human3.6M dataset with ground truth 2D detections, supervised training

Arch Receptive Field MPJPE P-MPJPE ckpt log
VideoPose3D 27 40.0 30.1 ckpt log
VideoPose3D 81 38.9 29.2 ckpt log
VideoPose3D 243 37.6 28.3 ckpt log

Results on Human3.6M dataset with CPN 2D detections1, supervised training

Arch Receptive Field MPJPE P-MPJPE ckpt log
VideoPose3D 1 52.9 41.3 ckpt log
VideoPose3D 243 47.9 38.0 ckpt log

Results on Human3.6M dataset with ground truth 2D detections, semi-supervised training

Training Data Arch Receptive Field MPJPE P-MPJPE N-MPJPE ckpt log
10% S1 VideoPose3D 27 58.1 42.8 54.7 ckpt log

Results on Human3.6M dataset with CPN 2D detections1, semi-supervised training

Training Data Arch Receptive Field MPJPE P-MPJPE N-MPJPE ckpt log
10% S1 VideoPose3D 27 67.4 50.1 63.2 ckpt log

1 CPN 2D detections are provided by official repo. The reformatted version used in this repository can be downloaded from train_detection and test_detection.


Video Pose Lift + Videopose3d on Mpi_inf_3dhp

VideoPose3D (CVPR'2019)
@inproceedings{pavllo20193d,
  title={3d human pose estimation in video with temporal convolutions and semi-supervised training},
  author={Pavllo, Dario and Feichtenhofer, Christoph and Grangier, David and Auli, Michael},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7753--7762},
  year={2019}
}
MPI-INF-3DHP (3DV'2017)
@inproceedings{mono-3dhp2017,
  author = {Mehta, Dushyant and Rhodin, Helge and Casas, Dan and Fua, Pascal and Sotnychenko, Oleksandr and Xu, Weipeng and Theobalt, Christian},
  title = {Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision},
  booktitle = {3D Vision (3DV), 2017 Fifth International Conference on},
  url = {http://gvv.mpi-inf.mpg.de/3dhp_dataset},
  year = {2017},
  organization={IEEE},
  doi={10.1109/3dv.2017.00064},
}

Results on MPI-INF-3DHP dataset with ground truth 2D detections, supervised training

Arch Receptive Field MPJPE P-MPJPE 3DPCK 3DAUC ckpt log
VideoPose3D 1 58.3 40.6 94.1 63.1 ckpt log



Hourglass (ECCV’2016)


Topdown Heatmap + Hourglass on Coco

Hourglass (ECCV'2016)
@inproceedings{newell2016stacked,
  title={Stacked hourglass networks for human pose estimation},
  author={Newell, Alejandro and Yang, Kaiyu and Deng, Jia},
  booktitle={European conference on computer vision},
  pages={483--499},
  year={2016},
  organization={Springer}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hourglass_52 256x256 0.726 0.896 0.799 0.780 0.934 ckpt log
pose_hourglass_52 384x384 0.746 0.900 0.813 0.797 0.939 ckpt log

Topdown Heatmap + Hourglass on Mpii

Hourglass (ECCV'2016)
@inproceedings{newell2016stacked,
  title={Stacked hourglass networks for human pose estimation},
  author={Newell, Alejandro and Yang, Kaiyu and Deng, Jia},
  booktitle={European conference on computer vision},
  pages={483--499},
  year={2016},
  organization={Springer}
}
MPII (CVPR'2014)
@inproceedings{andriluka14cvpr,
  author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt},
  title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2014},
  month = {June}
}

Results on MPII val set

Arch Input Size Mean Mean@0.1 ckpt log
pose_hourglass_52 256x256 0.889 0.317 ckpt log
pose_hourglass_52 384x384 0.894 0.366 ckpt log

Topdown Heatmap + Hourglass + Coco + Wholebody on Coco_wholebody_face

Hourglass (ECCV'2016)
@inproceedings{newell2016stacked,
  title={Stacked hourglass networks for human pose estimation},
  author={Newell, Alejandro and Yang, Kaiyu and Deng, Jia},
  booktitle={European conference on computer vision},
  pages={483--499},
  year={2016},
  organization={Springer}
}
COCO-WholeBody-Face (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody-Face val set

Arch Input Size NME ckpt log
pose_hourglass_52 256x256 0.0586 ckpt log

Topdown Heatmap + Hourglass + Coco + Wholebody on Coco_wholebody_hand

Hourglass (ECCV'2016)
@inproceedings{newell2016stacked,
  title={Stacked hourglass networks for human pose estimation},
  author={Newell, Alejandro and Yang, Kaiyu and Deng, Jia},
  booktitle={European conference on computer vision},
  pages={483--499},
  year={2016},
  organization={Springer}
}
COCO-WholeBody-Hand (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody-Hand val set

Arch Input Size PCK@0.2 AUC EPE ckpt log
pose_hourglass_52 256x256 0.804 0.835 4.54 ckpt log



LiteHRNet (CVPR’2021)


Topdown Heatmap + Litehrnet on Coco

LiteHRNet (CVPR'2021)
@inproceedings{Yulitehrnet21,
  title={Lite-HRNet: A Lightweight High-Resolution Network},
  author={Yu, Changqian and Xiao, Bin and Gao, Changxin and Yuan, Lu and Zhang, Lei and Sang, Nong and Wang, Jingdong},
  booktitle={CVPR},
  year={2021}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
LiteHRNet-18 256x192 0.643 0.868 0.720 0.706 0.912 ckpt log
LiteHRNet-18 384x288 0.677 0.878 0.746 0.735 0.920 ckpt log
LiteHRNet-30 256x192 0.675 0.881 0.754 0.736 0.924 ckpt log
LiteHRNet-30 384x288 0.700 0.884 0.776 0.758 0.928 ckpt log

Topdown Heatmap + Litehrnet on Mpii

LiteHRNet (CVPR'2021)
@inproceedings{Yulitehrnet21,
  title={Lite-HRNet: A Lightweight High-Resolution Network},
  author={Yu, Changqian and Xiao, Bin and Gao, Changxin and Yuan, Lu and Zhang, Lei and Sang, Nong and Wang, Jingdong},
  booktitle={CVPR},
  year={2021}
}
MPII (CVPR'2014)
@inproceedings{andriluka14cvpr,
  author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt},
  title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2014},
  month = {June}
}

Results on MPII val set

Arch Input Size Mean Mean@0.1 ckpt log
LiteHRNet-18 256x256 0.859 0.260 ckpt log
LiteHRNet-30 256x256 0.869 0.271 ckpt log

Topdown Heatmap + Litehrnet + Coco + Wholebody on Coco_wholebody_hand

LiteHRNet (CVPR'2021)
@inproceedings{Yulitehrnet21,
  title={Lite-HRNet: A Lightweight High-Resolution Network},
  author={Yu, Changqian and Xiao, Bin and Gao, Changxin and Yuan, Lu and Zhang, Lei and Sang, Nong and Wang, Jingdong},
  booktitle={CVPR},
  year={2021}
}
COCO-WholeBody-Hand (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody-Hand val set

Arch Input Size PCK@0.2 AUC EPE ckpt log
LiteHRNet-18 256x256 0.795 0.830 4.77 ckpt log



AdaptiveWingloss (ICCV’2019)


Topdown Heatmap + Hrnetv2 + Awing on WFLW

HRNetv2 (TPAMI'2019)
@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal={TPAMI},
  year={2019}
}
AdaptiveWingloss (ICCV'2019)
@inproceedings{wang2019adaptive,
  title={Adaptive wing loss for robust face alignment via heatmap regression},
  author={Wang, Xinyao and Bo, Liefeng and Fuxin, Li},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={6971--6981},
  year={2019}
}
WFLW (CVPR'2018)
@inproceedings{wu2018look,
  title={Look at boundary: A boundary-aware face alignment algorithm},
  author={Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, Quan and Cai, Yici and Zhou, Qiang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2129--2138},
  year={2018}
}

Results on WFLW dataset

The model is trained on WFLW train.

Arch Input Size NMEtest NMEpose NMEillumination NMEocclusion NMEblur NMEmakeup NMEexpression ckpt log
pose_hrnetv2_w18_awing 256x256 4.02 6.94 3.96 4.78 4.59 3.85 4.28 ckpt log



SimpleBaseline2D (ECCV’2018)


Topdown Heatmap + Resnet on Animalpose

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
Animal-Pose (ICCV'2019)
@InProceedings{Cao_2019_ICCV,
    author = {Cao, Jinkun and Tang, Hongyang and Fang, Hao-Shu and Shen, Xiaoyong and Lu, Cewu and Tai, Yu-Wing},
    title = {Cross-Domain Adaptation for Animal Pose Estimation},
    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
    month = {October},
    year = {2019}
}

Results on AnimalPose validation set (1117 instances)

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_resnet_50 256x256 0.688 0.945 0.772 0.733 0.952 ckpt log
pose_resnet_101 256x256 0.696 0.948 0.785 0.737 0.954 ckpt log
pose_resnet_152 256x256 0.709 0.948 0.797 0.749 0.951 ckpt log

Topdown Heatmap + Resnet on Ap10k

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
AP-10K (NeurIPS'2021)
@misc{yu2021ap10k,
      title={AP-10K: A Benchmark for Animal Pose Estimation in the Wild},
      author={Hang Yu and Yufei Xu and Jing Zhang and Wei Zhao and Ziyu Guan and Dacheng Tao},
      year={2021},
      eprint={2108.12617},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Results on AP-10K validation set

Arch Input Size AP AP50 AP75 APM APL ckpt log
pose_resnet_50 256x256 0.681 0.923 0.740 0.510 0.688 ckpt log
pose_resnet_101 256x256 0.681 0.922 0.742 0.534 0.688 ckpt log

Topdown Heatmap + Resnet on Atrw

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ATRW (ACM MM'2020)
@inproceedings{li2020atrw,
  title={ATRW: A Benchmark for Amur Tiger Re-identification in the Wild},
  author={Li, Shuyuan and Li, Jianguo and Tang, Hanlin and Qian, Rui and Lin, Weiyao},
  booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
  pages={2590--2598},
  year={2020}
}

Results on ATRW validation set

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_resnet_50 256x256 0.900 0.973 0.932 0.929 0.985 ckpt log
pose_resnet_101 256x256 0.898 0.973 0.936 0.927 0.985 ckpt log
pose_resnet_152 256x256 0.896 0.973 0.931 0.927 0.985 ckpt log

Topdown Heatmap + Resnet on Fly

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
Vinegar Fly (Nature Methods'2019)
@article{pereira2019fast,
  title={Fast animal pose estimation using deep neural networks},
  author={Pereira, Talmo D and Aldarondo, Diego E and Willmore, Lindsay and Kislin, Mikhail and Wang, Samuel S-H and Murthy, Mala and Shaevitz, Joshua W},
  journal={Nature methods},
  volume={16},
  number={1},
  pages={117--125},
  year={2019},
  publisher={Nature Publishing Group}
}

Results on Vinegar Fly test set

Arch Input Size PCK@0.2 AUC EPE ckpt log
pose_resnet_50 192x192 0.996 0.910 2.00 ckpt log
pose_resnet_101 192x192 0.996 0.912 1.95 ckpt log
pose_resnet_152 192x192 0.997 0.917 1.78 ckpt log

Topdown Heatmap + Resnet on Horse10

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
Horse-10 (WACV'2021)
@inproceedings{mathis2021pretraining,
  title={Pretraining boosts out-of-domain robustness for pose estimation},
  author={Mathis, Alexander and Biasi, Thomas and Schneider, Steffen and Yuksekgonul, Mert and Rogers, Byron and Bethge, Matthias and Mathis, Mackenzie W},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={1859--1868},
  year={2021}
}

Results on Horse-10 test set

Set Arch Input Size PCK@0.3 NME ckpt log
split1 pose_resnet_50 256x256 0.956 0.113 ckpt log
split2 pose_resnet_50 256x256 0.954 0.111 ckpt log
split3 pose_resnet_50 256x256 0.946 0.129 ckpt log
split1 pose_resnet_101 256x256 0.958 0.115 ckpt log
split2 pose_resnet_101 256x256 0.955 0.115 ckpt log
split3 pose_resnet_101 256x256 0.946 0.126 ckpt log
split1 pose_resnet_152 256x256 0.969 0.105 ckpt log
split2 pose_resnet_152 256x256 0.970 0.103 ckpt log
split3 pose_resnet_152 256x256 0.957 0.131 ckpt log

Topdown Heatmap + Resnet on Locust

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
Desert Locust (Elife'2019)
@article{graving2019deepposekit,
  title={DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning},
  author={Graving, Jacob M and Chae, Daniel and Naik, Hemal and Li, Liang and Koger, Benjamin and Costelloe, Blair R and Couzin, Iain D},
  journal={Elife},
  volume={8},
  pages={e47994},
  year={2019},
  publisher={eLife Sciences Publications Limited}
}

Results on Desert Locust test set

Arch Input Size PCK@0.2 AUC EPE ckpt log
pose_resnet_50 160x160 0.999 0.899 2.27 ckpt log
pose_resnet_101 160x160 0.999 0.907 2.03 ckpt log
pose_resnet_152 160x160 1.000 0.926 1.48 ckpt log

Topdown Heatmap + Resnet on Macaque

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
MacaquePose (bioRxiv'2020)
@article{labuguen2020macaquepose,
  title={MacaquePose: A novel ‘in the wild’macaque monkey pose dataset for markerless motion capture},
  author={Labuguen, Rollyn and Matsumoto, Jumpei and Negrete, Salvador and Nishimaru, Hiroshi and Nishijo, Hisao and Takada, Masahiko and Go, Yasuhiro and Inoue, Ken-ichi and Shibata, Tomohiro},
  journal={bioRxiv},
  year={2020},
  publisher={Cold Spring Harbor Laboratory}
}

Results on MacaquePose with ground-truth detection bounding boxes

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_resnet_50 256x192 0.799 0.952 0.919 0.837 0.964 ckpt log
pose_resnet_101 256x192 0.790 0.953 0.908 0.828 0.967 ckpt log
pose_resnet_152 256x192 0.794 0.951 0.915 0.834 0.968 ckpt log

Topdown Heatmap + Resnet on Zebra

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
Grévy’s Zebra (Elife'2019)
@article{graving2019deepposekit,
  title={DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning},
  author={Graving, Jacob M and Chae, Daniel and Naik, Hemal and Li, Liang and Koger, Benjamin and Costelloe, Blair R and Couzin, Iain D},
  journal={Elife},
  volume={8},
  pages={e47994},
  year={2019},
  publisher={eLife Sciences Publications Limited}
}

Results on Grévy’s Zebra test set

Arch Input Size PCK@0.2 AUC EPE ckpt log
pose_resnet_50 160x160 1.000 0.914 1.86 ckpt log
pose_resnet_101 160x160 1.000 0.916 1.82 ckpt log
pose_resnet_152 160x160 1.000 0.921 1.66 ckpt log

Topdown Heatmap + Resnet on Aic

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
AI Challenger (ArXiv'2017)
@article{wu2017ai,
  title={Ai challenger: A large-scale dataset for going deeper in image understanding},
  author={Wu, Jiahong and Zheng, He and Zhao, Bo and Li, Yixin and Yan, Baoming and Liang, Rui and Wang, Wenjia and Zhou, Shipei and Lin, Guosen and Fu, Yanwei and others},
  journal={arXiv preprint arXiv:1711.06475},
  year={2017}
}

Results on AIC val set with ground-truth bounding boxes

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_resnet_101 256x192 0.294 0.736 0.174 0.337 0.763 ckpt log

Topdown Heatmap + Resnet + Fp16 on Coco

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
FP16 (ArXiv'2017)
@article{micikevicius2017mixed,
  title={Mixed precision training},
  author={Micikevicius, Paulius and Narang, Sharan and Alben, Jonah and Diamos, Gregory and Elsen, Erich and Garcia, David and Ginsburg, Boris and Houston, Michael and Kuchaiev, Oleksii and Venkatesh, Ganesh and others},
  journal={arXiv preprint arXiv:1710.03740},
  year={2017}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_resnet_50_fp16 256x192 0.717 0.898 0.793 0.772 0.936 ckpt log

Topdown Heatmap + Resnet + Dark on Coco

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_resnet_50_dark 256x192 0.724 0.898 0.800 0.777 0.936 ckpt log
pose_resnet_50_dark 384x288 0.735 0.900 0.801 0.785 0.937 ckpt log
pose_resnet_101_dark 256x192 0.732 0.899 0.808 0.786 0.938 ckpt log
pose_resnet_101_dark 384x288 0.749 0.902 0.816 0.799 0.939 ckpt log
pose_resnet_152_dark 256x192 0.745 0.905 0.821 0.797 0.942 ckpt log
pose_resnet_152_dark 384x288 0.757 0.909 0.826 0.806 0.943 ckpt log

Topdown Heatmap + Swin on Coco

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
Swin (ICCV'2021)
@inproceedings{liu2021swin,
  title={Swin transformer: Hierarchical vision transformer using shifted windows},
  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={10012--10022},
  year={2021}
}
FPN (CVPR'2017)
@inproceedings{lin2017feature,
  title={Feature pyramid networks for object detection},
  author={Lin, Tsung-Yi and Doll{\'a}r, Piotr and Girshick, Ross and He, Kaiming and Hariharan, Bharath and Belongie, Serge},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2117--2125},
  year={2017}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_swin_t 256x192 0.724 0.901 0.806 0.782 0.940 ckpt log
pose_swin_b 256x192 0.737 0.904 0.820 0.798 0.946 ckpt log
pose_swin_b 384x288 0.759 0.910 0.832 0.811 0.946 ckpt log
pose_swin_l 256x192 0.743 0.906 0.821 0.798 0.943 ckpt log
pose_swin_l 384x288 0.763 0.912 0.830 0.814 0.949 ckpt log
pose_swin_b_fpn 256x192 0.741 0.907 0.821 0.798 0.946 ckpt log

Topdown Heatmap + Resnet on Coco

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_resnet_50 256x192 0.718 0.898 0.795 0.773 0.937 ckpt log
pose_resnet_50 384x288 0.731 0.900 0.799 0.783 0.931 ckpt log
pose_resnet_101 256x192 0.726 0.899 0.806 0.781 0.939 ckpt log
pose_resnet_101 384x288 0.748 0.905 0.817 0.798 0.940 ckpt log
pose_resnet_152 256x192 0.735 0.905 0.812 0.790 0.943 ckpt log
pose_resnet_152 384x288 0.750 0.908 0.821 0.800 0.942 ckpt log

Topdown Heatmap + Resnet on Crowdpose

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
CrowdPose (CVPR'2019)
@article{li2018crowdpose,
  title={CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark},
  author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},
  journal={arXiv preprint arXiv:1812.00324},
  year={2018}
}

Results on CrowdPose test with YOLOv3 human detector

Arch Input Size AP AP50 AP75 AP (E) AP (M) AP (H) ckpt log
pose_resnet_50 256x192 0.637 0.808 0.692 0.739 0.650 0.506 ckpt log
pose_resnet_101 256x192 0.647 0.810 0.703 0.744 0.658 0.522 ckpt log
pose_resnet_101 320x256 0.661 0.821 0.714 0.759 0.671 0.536 ckpt log
pose_resnet_152 256x192 0.656 0.818 0.712 0.754 0.666 0.532 ckpt log

Topdown Heatmap + Resnet on JHMDB

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
JHMDB (ICCV'2013)
@inproceedings{Jhuang:ICCV:2013,
  title = {Towards understanding action recognition},
  author = {H. Jhuang and J. Gall and S. Zuffi and C. Schmid and M. J. Black},
  booktitle = {International Conf. on Computer Vision (ICCV)},
  month = Dec,
  pages = {3192-3199},
  year = {2013}
}

Results on Sub-JHMDB dataset

The models are pre-trained on MPII dataset only. NO test-time augmentation (multi-scale /rotation testing) is used.

  • Normalized by Person Size

Split Arch Input Size Head Sho Elb Wri Hip Knee Ank Mean ckpt log
Sub1 pose_resnet_50 256x256 99.1 98.0 93.8 91.3 99.4 96.5 92.8 96.1 ckpt log
Sub2 pose_resnet_50 256x256 99.3 97.1 90.6 87.0 98.9 96.3 94.1 95.0 ckpt log
Sub3 pose_resnet_50 256x256 99.0 97.9 94.0 91.6 99.7 98.0 94.7 96.7 ckpt log
Average pose_resnet_50 256x256 99.2 97.7 92.8 90.0 99.3 96.9 93.9 96.0 - -
Sub1 pose_resnet_50 (2 Deconv.) 256x256 99.1 98.5 94.6 92.0 99.4 94.6 92.5 96.1 ckpt log
Sub2 pose_resnet_50 (2 Deconv.) 256x256 99.3 97.8 91.0 87.0 99.1 96.5 93.8 95.2 ckpt log
Sub3 pose_resnet_50 (2 Deconv.) 256x256 98.8 98.4 94.3 92.1 99.8 97.5 93.8 96.7 ckpt log
Average pose_resnet_50 (2 Deconv.) 256x256 99.1 98.2 93.3 90.4 99.4 96.2 93.4 96.0 - -
  • Normalized by Torso Size

Split Arch Input Size Head Sho Elb Wri Hip Knee Ank Mean ckpt log
Sub1 pose_resnet_50 256x256 93.3 83.2 74.4 72.7 85.0 81.2 78.9 81.9 ckpt log
Sub2 pose_resnet_50 256x256 94.1 74.9 64.5 62.5 77.9 71.9 78.6 75.5 ckpt log
Sub3 pose_resnet_50 256x256 97.0 82.2 74.9 70.7 84.7 83.7 84.2 82.9 ckpt log
Average pose_resnet_50 256x256 94.8 80.1 71.3 68.6 82.5 78.9 80.6 80.1 - -
Sub1 pose_resnet_50 (2 Deconv.) 256x256 92.4 80.6 73.2 70.5 82.3 75.4 75.0 79.2 ckpt log
Sub2 pose_resnet_50 (2 Deconv.) 256x256 93.4 73.6 63.8 60.5 75.1 68.4 75.5 73.7 ckpt log
Sub3 pose_resnet_50 (2 Deconv.) 256x256 96.1 81.2 72.6 67.9 83.6 80.9 81.5 81.2 ckpt log
Average pose_resnet_50 (2 Deconv.) 256x256 94.0 78.5 69.9 66.3 80.3 74.9 77.3 78.0 - -

Topdown Heatmap + Resnet on MHP

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
MHP (ACM MM'2018)
@inproceedings{zhao2018understanding,
  title={Understanding humans in crowded scenes: Deep nested adversarial learning and a new benchmark for multi-human parsing},
  author={Zhao, Jian and Li, Jianshu and Cheng, Yu and Sim, Terence and Yan, Shuicheng and Feng, Jiashi},
  booktitle={Proceedings of the 26th ACM international conference on Multimedia},
  pages={792--800},
  year={2018}
}

Results on MHP v2.0 val set

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_resnet_101 256x192 0.583 0.897 0.669 0.636 0.918 ckpt log

Note that, the evaluation metric used here is mAP (adapted from COCO), which may be different from the official evaluation codes. Please be cautious if you use the results in papers.


Topdown Heatmap + Resnet on Mpii

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
MPII (CVPR'2014)
@inproceedings{andriluka14cvpr,
  author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt},
  title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2014},
  month = {June}
}

Results on MPII val set

Arch Input Size Mean Mean@0.1 ckpt log
pose_resnet_50 256x256 0.882 0.286 ckpt log
pose_resnet_101 256x256 0.888 0.290 ckpt log
pose_resnet_152 256x256 0.889 0.303 ckpt log

Topdown Heatmap + Resnet + Mpii on Mpii_trb

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
MPII-TRB (ICCV'2019)
@inproceedings{duan2019trb,
  title={TRB: A Novel Triplet Representation for Understanding 2D Human Body},
  author={Duan, Haodong and Lin, Kwan-Yee and Jin, Sheng and Liu, Wentao and Qian, Chen and Ouyang, Wanli},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={9479--9488},
  year={2019}
}

Results on MPII-TRB val set

Arch Input Size Skeleton Acc Contour Acc Mean Acc ckpt log
pose_resnet_50 256x256 0.887 0.858 0.868 ckpt log
pose_resnet_101 256x256 0.890 0.863 0.873 ckpt log
pose_resnet_152 256x256 0.897 0.868 0.879 ckpt log

Topdown Heatmap + Resnet on Ochuman

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
OCHuman (CVPR'2019)
@inproceedings{zhang2019pose2seg,
  title={Pose2seg: Detection free human instance segmentation},
  author={Zhang, Song-Hai and Li, Ruilong and Dong, Xin and Rosin, Paul and Cai, Zixi and Han, Xi and Yang, Dingcheng and Huang, Haozhi and Hu, Shi-Min},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={889--898},
  year={2019}
}

Results on OCHuman test dataset with ground-truth bounding boxes

Following the common setting, the models are trained on COCO train dataset, and evaluate on OCHuman dataset.

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_resnet_50 256x192 0.546 0.726 0.593 0.592 0.755 ckpt log
pose_resnet_50 384x288 0.539 0.723 0.574 0.588 0.756 ckpt log
pose_resnet_101 256x192 0.559 0.724 0.606 0.605 0.751 ckpt log
pose_resnet_101 384x288 0.571 0.715 0.615 0.615 0.748 ckpt log
pose_resnet_152 256x192 0.570 0.725 0.617 0.616 0.754 ckpt log
pose_resnet_152 384x288 0.582 0.723 0.627 0.627 0.752 ckpt log

Topdown Heatmap + Resnet on Posetrack18

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
PoseTrack18 (CVPR'2018)
@inproceedings{andriluka2018posetrack,
  title={Posetrack: A benchmark for human pose estimation and tracking},
  author={Andriluka, Mykhaylo and Iqbal, Umar and Insafutdinov, Eldar and Pishchulin, Leonid and Milan, Anton and Gall, Juergen and Schiele, Bernt},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5167--5176},
  year={2018}
}

Results on PoseTrack2018 val with ground-truth bounding boxes

Arch Input Size Head Shou Elb Wri Hip Knee Ankl Total ckpt log
pose_resnet_50 256x192 86.5 87.5 82.3 75.6 79.9 78.6 74.0 81.0 ckpt log

The models are first pre-trained on COCO dataset, and then fine-tuned on PoseTrack18.

Results on PoseTrack2018 val with MMDetection pre-trained Cascade R-CNN (X-101-64x4d-FPN) human detector

Arch Input Size Head Shou Elb Wri Hip Knee Ankl Total ckpt log
pose_resnet_50 256x192 78.9 81.9 77.8 70.8 75.3 73.2 66.4 75.2 ckpt log

The models are first pre-trained on COCO dataset, and then fine-tuned on PoseTrack18.


Topdown Heatmap + Resnet + Coco + Wholebody on Coco_wholebody_face

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
COCO-WholeBody-Face (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody-Face val set

Arch Input Size NME ckpt log
pose_res50 256x256 0.0566 ckpt log

Topdown Heatmap + Resnet on Deepfashion

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
DeepFashion (CVPR'2016)
@inproceedings{liuLQWTcvpr16DeepFashion,
 author = {Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou},
 title = {DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations},
 booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 month = {June},
 year = {2016}
}
DeepFashion (ECCV'2016)
@inproceedings{liuYLWTeccv16FashionLandmark,
 author = {Liu, Ziwei and Yan, Sijie and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
 title = {Fashion Landmark Detection in the Wild},
 booktitle = {European Conference on Computer Vision (ECCV)},
 month = {October},
 year = {2016}
 }

Results on DeepFashion val set

Set Arch Input Size PCK@0.2 AUC EPE ckpt log
upper pose_resnet_50 256x256 0.954 0.578 16.8 ckpt log
lower pose_resnet_50 256x256 0.965 0.744 10.5 ckpt log
full pose_resnet_50 256x256 0.977 0.664 12.7 ckpt log

Topdown Heatmap + Resnet + Coco + Wholebody on Coco_wholebody_hand

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
COCO-WholeBody-Hand (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody-Hand val set

Arch Input Size PCK@0.2 AUC EPE ckpt log
pose_resnet_50 256x256 0.800 0.833 4.64 ckpt log

Topdown Heatmap + Resnet on Freihand2d

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
FreiHand (ICCV'2019)
@inproceedings{zimmermann2019freihand,
  title={Freihand: A dataset for markerless capture of hand pose and shape from single rgb images},
  author={Zimmermann, Christian and Ceylan, Duygu and Yang, Jimei and Russell, Bryan and Argus, Max and Brox, Thomas},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={813--822},
  year={2019}
}

Results on FreiHand val & test set

Set Arch Input Size PCK@0.2 AUC EPE ckpt log
val pose_resnet_50 224x224 0.993 0.868 3.25 ckpt log
test pose_resnet_50 224x224 0.992 0.868 3.27 ckpt log

Topdown Heatmap + Resnet on Interhand2d

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
InterHand2.6M (ECCV'2020)
@InProceedings{Moon_2020_ECCV_InterHand2.6M,
author = {Moon, Gyeongsik and Yu, Shoou-I and Wen, He and Shiratori, Takaaki and Lee, Kyoung Mu},
title = {InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2020}
}

Results on InterHand2.6M val & test set

Train Set Set Arch Input Size PCK@0.2 AUC EPE ckpt log
Human_annot val(M) pose_resnet_50 256x256 0.973 0.828 5.15 ckpt log
Human_annot test(H) pose_resnet_50 256x256 0.973 0.826 5.27 ckpt log
Human_annot test(M) pose_resnet_50 256x256 0.975 0.841 4.90 ckpt log
Human_annot test(H+M) pose_resnet_50 256x256 0.975 0.839 4.97 ckpt log
Machine_annot val(M) pose_resnet_50 256x256 0.970 0.824 5.39 ckpt log
Machine_annot test(H) pose_resnet_50 256x256 0.969 0.821 5.52 ckpt log
Machine_annot test(M) pose_resnet_50 256x256 0.972 0.838 5.03 ckpt log
Machine_annot test(H+M) pose_resnet_50 256x256 0.972 0.837 5.11 ckpt log
All val(M) pose_resnet_50 256x256 0.977 0.840 4.66 ckpt log
All test(H) pose_resnet_50 256x256 0.979 0.839 4.65 ckpt log
All test(M) pose_resnet_50 256x256 0.979 0.838 4.42 ckpt log
All test(H+M) pose_resnet_50 256x256 0.979 0.851 4.46 ckpt log

Topdown Heatmap + Resnet on Onehand10k

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
OneHand10K (TCSVT'2019)
@article{wang2018mask,
  title={Mask-pose cascaded cnn for 2d hand pose estimation from single color image},
  author={Wang, Yangang and Peng, Cong and Liu, Yebin},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  volume={29},
  number={11},
  pages={3258--3268},
  year={2018},
  publisher={IEEE}
}

Results on OneHand10K val set

Arch Input Size PCK@0.2 AUC EPE ckpt log
pose_resnet_50 256x256 0.989 0.555 25.19 ckpt log

Topdown Heatmap + Resnet on Panoptic2d

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
CMU Panoptic HandDB (CVPR'2017)
@inproceedings{simon2017hand,
  title={Hand keypoint detection in single images using multiview bootstrapping},
  author={Simon, Tomas and Joo, Hanbyul and Matthews, Iain and Sheikh, Yaser},
  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
  pages={1145--1153},
  year={2017}
}

Results on CMU Panoptic (MPII+NZSL val set)

Arch Input Size PCKh@0.7 AUC EPE ckpt log
pose_resnet_50 256x256 0.999 0.713 9.00 ckpt log

Topdown Heatmap + Resnet on Rhd2d

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
RHD (ICCV'2017)
@TechReport{zb2017hand,
  author={Christian Zimmermann and Thomas Brox},
  title={Learning to Estimate 3D Hand Pose from Single RGB Images},
  institution={arXiv:1705.01389},
  year={2017},
  note="https://arxiv.org/abs/1705.01389",
  url="https://lmb.informatik.uni-freiburg.de/projects/hand3d/"
}

Results on RHD test set

Arch Input Size PCK@0.2 AUC EPE ckpt log
pose_resnet50 256x256 0.991 0.898 2.33 ckpt log

Topdown Heatmap + Resnet on Coco-Wholebody

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
COCO-WholeBody (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody v1.0 val with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size Body AP Body AR Foot AP Foot AR Face AP Face AR Hand AP Hand AR Whole AP Whole AR ckpt log
pose_resnet_50 256x192 0.652 0.739 0.614 0.746 0.608 0.716 0.460 0.584 0.520 0.633 ckpt log
pose_resnet_50 384x288 0.666 0.747 0.635 0.763 0.732 0.812 0.537 0.647 0.573 0.671 ckpt log
pose_resnet_101 256x192 0.670 0.754 0.640 0.767 0.611 0.723 0.463 0.589 0.533 0.647 ckpt log
pose_resnet_101 384x288 0.692 0.770 0.680 0.798 0.747 0.822 0.549 0.658 0.597 0.692 ckpt log
pose_resnet_152 256x192 0.682 0.764 0.662 0.788 0.624 0.728 0.482 0.606 0.548 0.661 ckpt log
pose_resnet_152 384x288 0.703 0.780 0.693 0.813 0.751 0.825 0.559 0.667 0.610 0.705 ckpt log



PoseWarper (NeurIPS’2019)


Posewarper + Hrnet + Posetrack18 on Posetrack18

PoseWarper (NeurIPS'2019)
@inproceedings{NIPS2019_gberta,
title = {Learning Temporal Pose Estimation from Sparsely Labeled Videos},
author = {Bertasius, Gedas and Feichtenhofer, Christoph, and Tran, Du and Shi, Jianbo, and Torresani, Lorenzo},
booktitle = {Advances in Neural Information Processing Systems 33},
year = {2019},
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
PoseTrack18 (CVPR'2018)
@inproceedings{andriluka2018posetrack,
  title={Posetrack: A benchmark for human pose estimation and tracking},
  author={Andriluka, Mykhaylo and Iqbal, Umar and Insafutdinov, Eldar and Pishchulin, Leonid and Milan, Anton and Gall, Juergen and Schiele, Bernt},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5167--5176},
  year={2018}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Note that the training of PoseWarper can be split into two stages.

The first-stage is trained with the pre-trained checkpoint on COCO dataset, and the main backbone is fine-tuned on PoseTrack18 in a single-frame setting.

The second-stage is trained with the last checkpoint from the first stage, and the warping offsets are learned in a multi-frame setting while the backbone is frozen.

Results on PoseTrack2018 val with ground-truth bounding boxes

Arch Input Size Head Shou Elb Wri Hip Knee Ankl Total ckpt log
pose_hrnet_w48 384x288 88.2 90.3 86.1 81.6 81.8 83.8 81.5 85.0 ckpt log

Results on PoseTrack2018 val with precomputed human bounding boxes from PoseWarper supplementary data files from this link1.

Arch Input Size Head Shou Elb Wri Hip Knee Ankl Total ckpt log
pose_hrnet_w48 384x288 81.8 85.6 82.7 77.2 76.8 79.0 74.4 79.8 ckpt log

1 Please download the precomputed human bounding boxes on PoseTrack2018 val from $PoseWarper_supp_files/posetrack18_precomputed_boxes/val_boxes.json and place it here: $mmpose/data/posetrack18/posetrack18_precomputed_boxes/val_boxes.json to be consistent with the config. Please refer to DATA Preparation for more detail about data preparation.




SimpleBaseline3D (ICCV’2017)


Pose Lift + Simplebaseline3d on H36m

SimpleBaseline3D (ICCV'2017)
@inproceedings{martinez_2017_3dbaseline,
  title={A simple yet effective baseline for 3d human pose estimation},
  author={Martinez, Julieta and Hossain, Rayat and Romero, Javier and Little, James J.},
  booktitle={ICCV},
  year={2017}
}
Human3.6M (TPAMI'2014)
@article{h36m_pami,
  author = {Ionescu, Catalin and Papava, Dragos and Olaru, Vlad and Sminchisescu,  Cristian},
  title = {Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  publisher = {IEEE Computer Society},
  volume = {36},
  number = {7},
  pages = {1325-1339},
  month = {jul},
  year = {2014}
}

Results on Human3.6M dataset with ground truth 2D detections

Arch MPJPE P-MPJPE ckpt log
simple_baseline_3d_tcn1 43.4 34.3 ckpt log

1 Differing from the original paper, we didn’t apply the max-norm constraint because we found this led to a better convergence and performance.


Pose Lift + Simplebaseline3d on Mpi_inf_3dhp

SimpleBaseline3D (ICCV'2017)
@inproceedings{martinez_2017_3dbaseline,
  title={A simple yet effective baseline for 3d human pose estimation},
  author={Martinez, Julieta and Hossain, Rayat and Romero, Javier and Little, James J.},
  booktitle={ICCV},
  year={2017}
}
MPI-INF-3DHP (3DV'2017)
@inproceedings{mono-3dhp2017,
  author = {Mehta, Dushyant and Rhodin, Helge and Casas, Dan and Fua, Pascal and Sotnychenko, Oleksandr and Xu, Weipeng and Theobalt, Christian},
  title = {Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision},
  booktitle = {3D Vision (3DV), 2017 Fifth International Conference on},
  url = {http://gvv.mpi-inf.mpg.de/3dhp_dataset},
  year = {2017},
  organization={IEEE},
  doi={10.1109/3dv.2017.00064},
}

Results on MPI-INF-3DHP dataset with ground truth 2D detections

Arch MPJPE P-MPJPE 3DPCK 3DAUC ckpt log
simple_baseline_3d_tcn1 84.3 53.2 85.0 52.0 ckpt log

1 Differing from the original paper, we didn’t apply the max-norm constraint because we found this led to a better convergence and performance.




HMR (CVPR’2018)


HMR + Resnet on Mixed

HMR (CVPR'2018)
@inProceedings{kanazawaHMR18,
  title={End-to-end Recovery of Human Shape and Pose},
  author = {Angjoo Kanazawa
  and Michael J. Black
  and David W. Jacobs
  and Jitendra Malik},
  booktitle={Computer Vision and Pattern Recognition (CVPR)},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
Human3.6M (TPAMI'2014)
@article{h36m_pami,
  author = {Ionescu, Catalin and Papava, Dragos and Olaru, Vlad and Sminchisescu,  Cristian},
  title = {Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  publisher = {IEEE Computer Society},
  volume = {36},
  number = {7},
  pages = {1325-1339},
  month = {jul},
  year = {2014}
}

Results on Human3.6M with ground-truth bounding box having MPJPE-PA of 52.60 mm on Protocol2

Arch Input Size MPJPE (P1) MPJPE-PA (P1) MPJPE (P2) MPJPE-PA (P2) ckpt log
hmr_resnet_50 224x224 80.75 55.08 80.35 52.60 ckpt log



UDP (CVPR’2020)


Associative Embedding + Higherhrnet + Udp on Coco

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HigherHRNet (CVPR'2020)
@inproceedings{cheng2020higherhrnet,
  title={HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation},
  author={Cheng, Bowen and Xiao, Bin and Wang, Jingdong and Shi, Honghui and Huang, Thomas S and Zhang, Lei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5386--5395},
  year={2020}
}
UDP (CVPR'2020)
@InProceedings{Huang_2020_CVPR,
  author = {Huang, Junjie and Zhu, Zheng and Guo, Feng and Huang, Guan},
  title = {The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HigherHRNet-w32_udp 512x512 0.678 0.862 0.736 0.724 0.890 ckpt log
HigherHRNet-w48_udp 512x512 0.690 0.872 0.750 0.734 0.891 ckpt log

Associative Embedding + Hrnet + Udp on Coco

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
UDP (CVPR'2020)
@InProceedings{Huang_2020_CVPR,
  author = {Huang, Junjie and Zhu, Zheng and Guo, Feng and Huang, Guan},
  title = {The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32_udp 512x512 0.671 0.863 0.729 0.717 0.889 ckpt log
HRNet-w48_udp 512x512 0.681 0.872 0.741 0.725 0.892 ckpt log

Topdown Heatmap + Hrnet + Udp on Coco

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
UDP (CVPR'2020)
@InProceedings{Huang_2020_CVPR,
  author = {Huang, Junjie and Zhu, Zheng and Guo, Feng and Huang, Guan},
  title = {The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hrnet_w32_udp 256x192 0.760 0.907 0.827 0.811 0.945 ckpt log
pose_hrnet_w32_udp 384x288 0.769 0.908 0.833 0.817 0.944 ckpt log
pose_hrnet_w48_udp 256x192 0.767 0.906 0.834 0.817 0.945 ckpt log
pose_hrnet_w48_udp 384x288 0.772 0.910 0.835 0.820 0.945 ckpt log
pose_hrnet_w32_udp_regress 256x192 0.758 0.908 0.823 0.812 0.943 ckpt log

Note that, UDP also adopts the unbiased encoding/decoding algorithm of DARK.


Topdown Heatmap + Hrnetv2 + Udp on Onehand10k

HRNetv2 (TPAMI'2019)
@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal={TPAMI},
  year={2019}
}
UDP (CVPR'2020)
@InProceedings{Huang_2020_CVPR,
  author = {Huang, Junjie and Zhu, Zheng and Guo, Feng and Huang, Guan},
  title = {The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}
OneHand10K (TCSVT'2019)
@article{wang2018mask,
  title={Mask-pose cascaded cnn for 2d hand pose estimation from single color image},
  author={Wang, Yangang and Peng, Cong and Liu, Yebin},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  volume={29},
  number={11},
  pages={3258--3268},
  year={2018},
  publisher={IEEE}
}

Results on OneHand10K val set

Arch Input Size PCK@0.2 AUC EPE ckpt log
pose_hrnetv2_w18_udp 256x256 0.990 0.572 23.87 ckpt log

Topdown Heatmap + Hrnetv2 + Udp on Panoptic2d

HRNetv2 (TPAMI'2019)
@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal={TPAMI},
  year={2019}
}
UDP (CVPR'2020)
@InProceedings{Huang_2020_CVPR,
  author = {Huang, Junjie and Zhu, Zheng and Guo, Feng and Huang, Guan},
  title = {The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}
CMU Panoptic HandDB (CVPR'2017)
@inproceedings{simon2017hand,
  title={Hand keypoint detection in single images using multiview bootstrapping},
  author={Simon, Tomas and Joo, Hanbyul and Matthews, Iain and Sheikh, Yaser},
  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
  pages={1145--1153},
  year={2017}
}

Results on CMU Panoptic (MPII+NZSL val set)

Arch Input Size PCKh@0.7 AUC EPE ckpt log
pose_hrnetv2_w18_udp 256x256 0.998 0.742 7.84 ckpt log

Topdown Heatmap + Hrnetv2 + Udp on Rhd2d

HRNetv2 (TPAMI'2019)
@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal={TPAMI},
  year={2019}
}
UDP (CVPR'2020)
@InProceedings{Huang_2020_CVPR,
  author = {Huang, Junjie and Zhu, Zheng and Guo, Feng and Huang, Guan},
  title = {The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}
RHD (ICCV'2017)
@TechReport{zb2017hand,
  author={Christian Zimmermann and Thomas Brox},
  title={Learning to Estimate 3D Hand Pose from Single RGB Images},
  institution={arXiv:1705.01389},
  year={2017},
  note="https://arxiv.org/abs/1705.01389",
  url="https://lmb.informatik.uni-freiburg.de/projects/hand3d/"
}

Results on RHD test set

Arch Input Size PCKh@0.7 AUC EPE ckpt log
pose_hrnetv2_w18_udp 256x256 0.992 0.902 2.21 ckpt log



ViPNAS (CVPR’2021)


Topdown Heatmap + Vipnas on Coco

ViPNAS (CVPR'2021)
@article{xu2021vipnas,
  title={ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search},
  author={Xu, Lumin and Guan, Yingda and Jin, Sheng and Liu, Wentao and Qian, Chen and Luo, Ping and Ouyang, Wanli and Wang, Xiaogang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  year={2021}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
S-ViPNAS-MobileNetV3 256x192 0.700 0.887 0.778 0.757 0.929 ckpt log
S-ViPNAS-Res50 256x192 0.711 0.893 0.789 0.769 0.934 ckpt log

Topdown Heatmap + Vipnas on Coco-Wholebody

ViPNAS (CVPR'2021)
@article{xu2021vipnas,
  title={ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search},
  author={Xu, Lumin and Guan, Yingda and Jin, Sheng and Liu, Wentao and Qian, Chen and Luo, Ping and Ouyang, Wanli and Wang, Xiaogang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  year={2021}
}
COCO-WholeBody (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody v1.0 val with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size Body AP Body AR Foot AP Foot AR Face AP Face AR Hand AP Hand AR Whole AP Whole AR ckpt log
S-ViPNAS-MobileNetV3 256x192 0.619 0.700 0.477 0.608 0.585 0.689 0.386 0.505 0.473 0.578 ckpt log
S-ViPNAS-Res50 256x192 0.643 0.726 0.553 0.694 0.587 0.698 0.410 0.529 0.495 0.607 ckpt log

Topdown Heatmap + Vipnas + Dark on Coco-Wholebody

ViPNAS (CVPR'2021)
@article{xu2021vipnas,
  title={ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search},
  author={Xu, Lumin and Guan, Yingda and Jin, Sheng and Liu, Wentao and Qian, Chen and Luo, Ping and Ouyang, Wanli and Wang, Xiaogang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  year={2021}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
COCO-WholeBody (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody v1.0 val with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size Body AP Body AR Foot AP Foot AR Face AP Face AR Hand AP Hand AR Whole AP Whole AR ckpt log
S-ViPNAS-MobileNetV3_dark 256x192 0.632 0.710 0.530 0.660 0.672 0.771 0.404 0.519 0.508 0.607 ckpt log
S-ViPNAS-Res50_dark 256x192 0.650 0.732 0.550 0.686 0.684 0.784 0.437 0.554 0.528 0.632 ckpt log



Wingloss (CVPR’2018)


Deeppose + Resnet + Wingloss on WFLW

DeepPose (CVPR'2014)
@inproceedings{toshev2014deeppose,
  title={Deeppose: Human pose estimation via deep neural networks},
  author={Toshev, Alexander and Szegedy, Christian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1653--1660},
  year={2014}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
Wingloss (CVPR'2018)
@inproceedings{feng2018wing,
  title={Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks},
  author={Feng, Zhen-Hua and Kittler, Josef and Awais, Muhammad and Huber, Patrik and Wu, Xiao-Jun},
  booktitle={Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on},
  year={2018},
  pages ={2235-2245},
  organization={IEEE}
}
WFLW (CVPR'2018)
@inproceedings{wu2018look,
  title={Look at boundary: A boundary-aware face alignment algorithm},
  author={Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, Quan and Cai, Yici and Zhou, Qiang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2129--2138},
  year={2018}
}

Results on WFLW dataset

The model is trained on WFLW train.

Arch Input Size NMEtest NMEpose NMEillumination NMEocclusion NMEblur NMEmakeup NMEexpression ckpt log
deeppose_res50_wingloss 256x256 4.64 8.25 4.59 5.56 5.26 4.59 5.07 ckpt log



DarkPose (CVPR’2020)


Topdown Heatmap + Resnet + Dark on Coco

SimpleBaseline2D (ECCV'2018)
@inproceedings{xiao2018simple,
  title={Simple baselines for human pose estimation and tracking},
  author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={466--481},
  year={2018}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_resnet_50_dark 256x192 0.724 0.898 0.800 0.777 0.936 ckpt log
pose_resnet_50_dark 384x288 0.735 0.900 0.801 0.785 0.937 ckpt log
pose_resnet_101_dark 256x192 0.732 0.899 0.808 0.786 0.938 ckpt log
pose_resnet_101_dark 384x288 0.749 0.902 0.816 0.799 0.939 ckpt log
pose_resnet_152_dark 256x192 0.745 0.905 0.821 0.797 0.942 ckpt log
pose_resnet_152_dark 384x288 0.757 0.909 0.826 0.806 0.943 ckpt log

Topdown Heatmap + Hrnet + Dark on Coco

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hrnet_w32_dark 256x192 0.757 0.907 0.823 0.808 0.943 ckpt log
pose_hrnet_w32_dark 384x288 0.766 0.907 0.831 0.815 0.943 ckpt log
pose_hrnet_w48_dark 256x192 0.764 0.907 0.830 0.814 0.943 ckpt log
pose_hrnet_w48_dark 384x288 0.772 0.910 0.836 0.820 0.946 ckpt log

Topdown Heatmap + Hrnet + Dark on Mpii

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
MPII (CVPR'2014)
@inproceedings{andriluka14cvpr,
  author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt},
  title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2014},
  month = {June}
}

Results on MPII val set

Arch Input Size Mean Mean@0.1 ckpt log
pose_hrnet_w32_dark 256x256 0.904 0.354 ckpt log
pose_hrnet_w48_dark 256x256 0.905 0.360 ckpt log

Topdown Heatmap + Hrnetv2 + Dark on Aflw

HRNetv2 (TPAMI'2019)
@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal={TPAMI},
  year={2019}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
AFLW (ICCVW'2011)
@inproceedings{koestinger2011annotated,
  title={Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization},
  author={Koestinger, Martin and Wohlhart, Paul and Roth, Peter M and Bischof, Horst},
  booktitle={2011 IEEE international conference on computer vision workshops (ICCV workshops)},
  pages={2144--2151},
  year={2011},
  organization={IEEE}
}

Results on AFLW dataset

The model is trained on AFLW train and evaluated on AFLW full and frontal.

Arch Input Size NMEfull NMEfrontal ckpt log
pose_hrnetv2_w18_dark 256x256 1.34 1.20 ckpt log

Topdown Heatmap + Hrnetv2 + Dark + Coco + Wholebody on Coco_wholebody_face

HRNetv2 (TPAMI'2019)
@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal={TPAMI},
  year={2019}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
COCO-WholeBody-Face (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody-Face val set

Arch Input Size NME ckpt log
pose_hrnetv2_w18_dark 256x256 0.0513 ckpt log

Topdown Heatmap + Hrnetv2 + Dark on WFLW

HRNetv2 (TPAMI'2019)
@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal={TPAMI},
  year={2019}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
WFLW (CVPR'2018)
@inproceedings{wu2018look,
  title={Look at boundary: A boundary-aware face alignment algorithm},
  author={Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, Quan and Cai, Yici and Zhou, Qiang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2129--2138},
  year={2018}
}

Results on WFLW dataset

The model is trained on WFLW train.

Arch Input Size NMEtest NMEpose NMEillumination NMEocclusion NMEblur NMEmakeup NMEexpression ckpt log
pose_hrnetv2_w18_dark 256x256 3.98 6.99 3.96 4.78 4.57 3.87 4.30 ckpt log

Topdown Heatmap + Hrnetv2 + Dark + Coco + Wholebody on Coco_wholebody_hand

HRNetv2 (TPAMI'2019)
@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal={TPAMI},
  year={2019}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
COCO-WholeBody-Hand (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody-Hand val set

Arch Input Size PCK@0.2 AUC EPE ckpt log
pose_hrnetv2_w18_dark 256x256 0.814 0.840 4.37 ckpt log

Topdown Heatmap + Hrnetv2 + Dark on Onehand10k

HRNetv2 (TPAMI'2019)
@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal={TPAMI},
  year={2019}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
OneHand10K (TCSVT'2019)
@article{wang2018mask,
  title={Mask-pose cascaded cnn for 2d hand pose estimation from single color image},
  author={Wang, Yangang and Peng, Cong and Liu, Yebin},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  volume={29},
  number={11},
  pages={3258--3268},
  year={2018},
  publisher={IEEE}
}

Results on OneHand10K val set

Arch Input Size PCK@0.2 AUC EPE ckpt log
pose_hrnetv2_w18_dark 256x256 0.990 0.573 23.84 ckpt log

Topdown Heatmap + Hrnetv2 + Dark on Panoptic2d

HRNetv2 (TPAMI'2019)
@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal={TPAMI},
  year={2019}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
CMU Panoptic HandDB (CVPR'2017)
@inproceedings{simon2017hand,
  title={Hand keypoint detection in single images using multiview bootstrapping},
  author={Simon, Tomas and Joo, Hanbyul and Matthews, Iain and Sheikh, Yaser},
  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
  pages={1145--1153},
  year={2017}
}

Results on CMU Panoptic (MPII+NZSL val set)

Arch Input Size PCKh@0.7 AUC EPE ckpt log
pose_hrnetv2_w18_dark 256x256 0.999 0.745 7.77 ckpt log

Topdown Heatmap + Hrnetv2 + Dark on Rhd2d

HRNetv2 (TPAMI'2019)
@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal={TPAMI},
  year={2019}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
RHD (ICCV'2017)
@TechReport{zb2017hand,
  author={Christian Zimmermann and Thomas Brox},
  title={Learning to Estimate 3D Hand Pose from Single RGB Images},
  institution={arXiv:1705.01389},
  year={2017},
  note="https://arxiv.org/abs/1705.01389",
  url="https://lmb.informatik.uni-freiburg.de/projects/hand3d/"
}

Results on RHD test set

Arch Input Size PCK@0.2 AUC EPE ckpt log
pose_hrnetv2_w18_dark 256x256 0.992 0.903 2.17 ckpt log

Topdown Heatmap + Vipnas + Dark on Coco-Wholebody

ViPNAS (CVPR'2021)
@article{xu2021vipnas,
  title={ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search},
  author={Xu, Lumin and Guan, Yingda and Jin, Sheng and Liu, Wentao and Qian, Chen and Luo, Ping and Ouyang, Wanli and Wang, Xiaogang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  year={2021}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
COCO-WholeBody (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody v1.0 val with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size Body AP Body AR Foot AP Foot AR Face AP Face AR Hand AP Hand AR Whole AP Whole AR ckpt log
S-ViPNAS-MobileNetV3_dark 256x192 0.632 0.710 0.530 0.660 0.672 0.771 0.404 0.519 0.508 0.607 ckpt log
S-ViPNAS-Res50_dark 256x192 0.650 0.732 0.550 0.686 0.684 0.784 0.437 0.554 0.528 0.632 ckpt log

Topdown Heatmap + Hrnet + Dark on Coco-Wholebody

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
COCO-WholeBody (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody v1.0 val with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size Body AP Body AR Foot AP Foot AR Face AP Face AR Hand AP Hand AR Whole AP Whole AR ckpt log
pose_hrnet_w32_dark 256x192 0.694 0.764 0.565 0.674 0.736 0.808 0.503 0.602 0.582 0.671 ckpt log
pose_hrnet_w48_dark+ 384x288 0.742 0.807 0.705 0.804 0.840 0.892 0.602 0.694 0.661 0.743 ckpt log

Note: + means the model is first pre-trained on original COCO dataset, and then fine-tuned on COCO-WholeBody dataset. We find this will lead to better performance.


Topdown Heatmap + Hrnet + Dark on Halpe

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
Halpe (CVPR'2020)
@inproceedings{li2020pastanet,
  title={PaStaNet: Toward Human Activity Knowledge Engine},
  author={Li, Yong-Lu and Xu, Liang and Liu, Xinpeng and Huang, Xijie and Xu, Yue and Wang, Shiyi and Fang, Hao-Shu and Ma, Ze and Chen, Mingyang and Lu, Cewu},
  booktitle={CVPR},
  year={2020}
}

Results on Halpe v1.0 val with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size Whole AP Whole AR ckpt log
pose_hrnet_w48_dark+ 384x288 0.527 0.620 ckpt log

Note: + means the model is first pre-trained on original COCO dataset, and then fine-tuned on Halpe dataset. We find this will lead to better performance.




Associative Embedding (NIPS’2017)


Associative Embedding + Hrnet on Aic

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
AI Challenger (ArXiv'2017)
@article{wu2017ai,
  title={Ai challenger: A large-scale dataset for going deeper in image understanding},
  author={Wu, Jiahong and Zheng, He and Zhao, Bo and Li, Yixin and Yan, Baoming and Liang, Rui and Wang, Wenjia and Zhou, Shipei and Lin, Guosen and Fu, Yanwei and others},
  journal={arXiv preprint arXiv:1711.06475},
  year={2017}
}

Results on AIC validation set without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32 512x512 0.303 0.697 0.225 0.373 0.755 ckpt log

Results on AIC validation set with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32 512x512 0.318 0.717 0.246 0.379 0.764 ckpt log

Associative Embedding + Higherhrnet on Aic

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HigherHRNet (CVPR'2020)
@inproceedings{cheng2020higherhrnet,
  title={HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation},
  author={Cheng, Bowen and Xiao, Bin and Wang, Jingdong and Shi, Honghui and Huang, Thomas S and Zhang, Lei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5386--5395},
  year={2020}
}
AI Challenger (ArXiv'2017)
@article{wu2017ai,
  title={Ai challenger: A large-scale dataset for going deeper in image understanding},
  author={Wu, Jiahong and Zheng, He and Zhao, Bo and Li, Yixin and Yan, Baoming and Liang, Rui and Wang, Wenjia and Zhou, Shipei and Lin, Guosen and Fu, Yanwei and others},
  journal={arXiv preprint arXiv:1711.06475},
  year={2017}
}

Results on AIC validation set without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HigherHRNet-w32 512x512 0.315 0.710 0.243 0.379 0.757 ckpt log

Results on AIC validation set with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HigherHRNet-w32 512x512 0.323 0.718 0.254 0.379 0.758 ckpt log

Associative Embedding + Higherhrnet + Udp on Coco

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HigherHRNet (CVPR'2020)
@inproceedings{cheng2020higherhrnet,
  title={HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation},
  author={Cheng, Bowen and Xiao, Bin and Wang, Jingdong and Shi, Honghui and Huang, Thomas S and Zhang, Lei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5386--5395},
  year={2020}
}
UDP (CVPR'2020)
@InProceedings{Huang_2020_CVPR,
  author = {Huang, Junjie and Zhu, Zheng and Guo, Feng and Huang, Guan},
  title = {The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HigherHRNet-w32_udp 512x512 0.678 0.862 0.736 0.724 0.890 ckpt log
HigherHRNet-w48_udp 512x512 0.690 0.872 0.750 0.734 0.891 ckpt log

Associative Embedding + Mobilenetv2 on Coco

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
MobilenetV2 (CVPR'2018)
@inproceedings{sandler2018mobilenetv2,
  title={Mobilenetv2: Inverted residuals and linear bottlenecks},
  author={Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4510--4520},
  year={2018}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_mobilenetv2 512x512 0.380 0.671 0.368 0.473 0.741 ckpt log

Results on COCO val2017 with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_mobilenetv2 512x512 0.442 0.696 0.422 0.517 0.766 ckpt log

Associative Embedding + Hrnet on Coco

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32 512x512 0.654 0.863 0.720 0.710 0.892 ckpt log
HRNet-w48 512x512 0.665 0.860 0.727 0.716 0.889 ckpt log

Results on COCO val2017 with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32 512x512 0.698 0.877 0.760 0.748 0.907 ckpt log
HRNet-w48 512x512 0.712 0.880 0.771 0.757 0.909 ckpt log

Associative Embedding + Higherhrnet on Coco

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HigherHRNet (CVPR'2020)
@inproceedings{cheng2020higherhrnet,
  title={HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation},
  author={Cheng, Bowen and Xiao, Bin and Wang, Jingdong and Shi, Honghui and Huang, Thomas S and Zhang, Lei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5386--5395},
  year={2020}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HigherHRNet-w32 512x512 0.677 0.870 0.738 0.723 0.890 ckpt log
HigherHRNet-w32 640x640 0.686 0.871 0.747 0.733 0.898 ckpt log
HigherHRNet-w48 512x512 0.686 0.873 0.741 0.731 0.892 ckpt log

Results on COCO val2017 with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HigherHRNet-w32 512x512 0.706 0.881 0.771 0.747 0.901 ckpt log
HigherHRNet-w32 640x640 0.706 0.880 0.770 0.749 0.902 ckpt log
HigherHRNet-w48 512x512 0.716 0.884 0.775 0.755 0.901 ckpt log

Associative Embedding + Resnet on Coco

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
ResNet (CVPR'2016)
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_resnet_50 512x512 0.466 0.742 0.479 0.552 0.797 ckpt log
pose_resnet_50 640x640 0.479 0.757 0.487 0.566 0.810 ckpt log
pose_resnet_101 512x512 0.554 0.807 0.599 0.622 0.841 ckpt log
pose_resnet_152 512x512 0.595 0.829 0.648 0.651 0.856 ckpt log

Results on COCO val2017 with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_resnet_50 512x512 0.503 0.765 0.521 0.591 0.821 ckpt log
pose_resnet_50 640x640 0.525 0.784 0.542 0.610 0.832 ckpt log
pose_resnet_101 512x512 0.603 0.831 0.641 0.668 0.870 ckpt log
pose_resnet_152 512x512 0.660 0.860 0.713 0.709 0.889 ckpt log

Associative Embedding + Hrnet + Udp on Coco

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
UDP (CVPR'2020)
@InProceedings{Huang_2020_CVPR,
  author = {Huang, Junjie and Zhu, Zheng and Guo, Feng and Huang, Guan},
  title = {The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32_udp 512x512 0.671 0.863 0.729 0.717 0.889 ckpt log
HRNet-w48_udp 512x512 0.681 0.872 0.741 0.725 0.892 ckpt log

Associative Embedding + Hourglass + Ae on Coco

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HourglassAENet (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hourglass_ae 512x512 0.613 0.833 0.667 0.659 0.850 ckpt log

Results on COCO val2017 with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hourglass_ae 512x512 0.667 0.855 0.723 0.707 0.877 ckpt log

Associative Embedding + Higherhrnet on Crowdpose

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HigherHRNet (CVPR'2020)
@inproceedings{cheng2020higherhrnet,
  title={HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation},
  author={Cheng, Bowen and Xiao, Bin and Wang, Jingdong and Shi, Honghui and Huang, Thomas S and Zhang, Lei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5386--5395},
  year={2020}
}
CrowdPose (CVPR'2019)
@article{li2018crowdpose,
  title={CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark},
  author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},
  journal={arXiv preprint arXiv:1812.00324},
  year={2018}
}

Results on CrowdPose test without multi-scale test

Arch Input Size AP AP50 AP75 AP (E) AP (M) AP (H) ckpt log
HigherHRNet-w32 512x512 0.655 0.859 0.705 0.728 0.660 0.577 ckpt log

Results on CrowdPose test with multi-scale test. 2 scales ([2, 1]) are used

Arch Input Size AP AP50 AP75 AP (E) AP (M) AP (H) ckpt log
HigherHRNet-w32 512x512 0.661 0.864 0.710 0.742 0.670 0.566 ckpt log

Associative Embedding + Hrnet on MHP

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
MHP (ACM MM'2018)
@inproceedings{zhao2018understanding,
  title={Understanding humans in crowded scenes: Deep nested adversarial learning and a new benchmark for multi-human parsing},
  author={Zhao, Jian and Li, Jianshu and Cheng, Yu and Sim, Terence and Yan, Shuicheng and Feng, Jiashi},
  booktitle={Proceedings of the 26th ACM international conference on Multimedia},
  pages={792--800},
  year={2018}
}

Results on MHP v2.0 validation set without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w48 512x512 0.583 0.895 0.666 0.656 0.931 ckpt log

Results on MHP v2.0 validation set with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w48 512x512 0.592 0.898 0.673 0.664 0.932 ckpt log

Associative Embedding + Hrnet on Coco-Wholebody

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
COCO-WholeBody (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody v1.0 val without multi-scale test

Arch Input Size Body AP Body AR Foot AP Foot AR Face AP Face AR Hand AP Hand AR Whole AP Whole AR ckpt log
HRNet-w32+ 512x512 0.551 0.650 0.271 0.451 0.564 0.618 0.159 0.238 0.342 0.453 ckpt log
HRNet-w48+ 512x512 0.592 0.686 0.443 0.595 0.619 0.674 0.347 0.438 0.422 0.532 ckpt log

Note: + means the model is first pre-trained on original COCO dataset, and then fine-tuned on COCO-WholeBody dataset. We find this will lead to better performance.


Associative Embedding + Higherhrnet on Coco-Wholebody

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HigherHRNet (CVPR'2020)
@inproceedings{cheng2020higherhrnet,
  title={HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation},
  author={Cheng, Bowen and Xiao, Bin and Wang, Jingdong and Shi, Honghui and Huang, Thomas S and Zhang, Lei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5386--5395},
  year={2020}
}
COCO-WholeBody (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody v1.0 val without multi-scale test

Arch Input Size Body AP Body AR Foot AP Foot AR Face AP Face AR Hand AP Hand AR Whole AP Whole AR ckpt log
HigherHRNet-w32+ 512x512 0.590 0.672 0.185 0.335 0.676 0.721 0.212 0.298 0.401 0.493 ckpt log
HigherHRNet-w48+ 512x512 0.630 0.706 0.440 0.573 0.730 0.777 0.389 0.477 0.487 0.574 ckpt log

Note: + means the model is first pre-trained on original COCO dataset, and then fine-tuned on COCO-WholeBody dataset. We find this will lead to better performance.




VoxelPose (ECCV’2020)


Voxelpose + Voxelpose on Campus

VoxelPose (ECCV'2020)
@inproceedings{tumultipose,
  title={VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment},
  author={Tu, Hanyue and Wang, Chunyu and Zeng, Wenjun},
  booktitle={ECCV},
  year={2020}
}
Campus (CVPR'2014)
@inproceedings {belagian14multi,
    title = {{3D} Pictorial Structures for Multiple Human Pose Estimation},
    author = {Belagiannis, Vasileios and Amin, Sikandar and Andriluka, Mykhaylo and Schiele, Bernt and Navab
    Nassir and Ilic, Slobodan},
    booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2014},
    month = {June},
    organization={IEEE}
}

Results on Campus dataset.

Arch Actor 1 Actor 2 Actor 3 Average ckpt log
prn32_cpn80_res50 97.76 93.92 98.48 96.72 ckpt log
prn64_cpn80_res50 97.76 93.33 98.77 96.62 ckpt log

Voxelpose + Voxelpose + Prn64x64x64 + Cpn80x80x20 + Panoptic on Panoptic

VoxelPose (ECCV'2020)
@inproceedings{tumultipose,
  title={VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment},
  author={Tu, Hanyue and Wang, Chunyu and Zeng, Wenjun},
  booktitle={ECCV},
  year={2020}
}
CMU Panoptic (ICCV'2015)
@Article = {joo_iccv_2015,
author = {Hanbyul Joo, Hao Liu, Lei Tan, Lin Gui, Bart Nabbe, Iain Matthews, Takeo Kanade, Shohei Nobuhara, and Yaser Sheikh},
title = {Panoptic Studio: A Massively Multiview System for Social Motion Capture},
booktitle = {ICCV},
year = {2015}
}

Results on CMU Panoptic dataset.

Arch mAP mAR MPJPE Recall@500mm ckpt log
prn64_cpn80_res50 97.31 97.99 17.57 99.85 ckpt log

Voxelpose + Voxelpose on Shelf

VoxelPose (ECCV'2020)
@inproceedings{tumultipose,
  title={VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment},
  author={Tu, Hanyue and Wang, Chunyu and Zeng, Wenjun},
  booktitle={ECCV},
  year={2020}
}
Shelf (CVPR'2014)
@inproceedings {belagian14multi,
    title = {{3D} Pictorial Structures for Multiple Human Pose Estimation},
    author = {Belagiannis, Vasileios and Amin, Sikandar and Andriluka, Mykhaylo and Schiele, Bernt and Navab
    Nassir and Ilic, Slobo
    booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2014},
    month = {June},
    organization={IEEE}
}

Results on Shelf dataset.

Arch Actor 1 Actor 2 Actor 3 Average ckpt log
prn32_cpn48_res50 99.10 94.86 97.52 97.16 ckpt log
prn64_cpn80_res50 99.00 94.59 97.64 97.08 ckpt log



RSN (ECCV’2020)


Topdown Heatmap + RSN on Coco

RSN (ECCV'2020)
@misc{cai2020learning,
    title={Learning Delicate Local Representations for Multi-Person Pose Estimation},
    author={Yuanhao Cai and Zhicheng Wang and Zhengxiong Luo and Binyi Yin and Angang Du and Haoqian Wang and Xinyu Zhou and Erjin Zhou and Xiangyu Zhang and Jian Sun},
    year={2020},
    eprint={2003.04030},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
rsn_18 256x192 0.704 0.887 0.779 0.771 0.926 ckpt log
rsn_50 256x192 0.723 0.896 0.800 0.788 0.934 ckpt log
2xrsn_50 256x192 0.745 0.899 0.818 0.809 0.939 ckpt log
3xrsn_50 256x192 0.750 0.900 0.823 0.813 0.940 ckpt log



CID (CVPR’2022)


Cid + Hrnet on Coco

CID (CVPR'2022)
@InProceedings{Wang_2022_CVPR,
    author    = {Wang, Dongkai and Zhang, Shiliang},
    title     = {Contextual Instance Decoupling for Robust Multi-Person Pose Estimation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {11060-11068}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
CID 512x512 0.702 0.887 0.768 0.755 0.926 ckpt log
CID 512x512 0.715 0.895 0.780 0.768 0.932 ckpt log



CPM (CVPR’2016)


Topdown Heatmap + CPM on Coco

CPM (CVPR'2016)
@inproceedings{wei2016convolutional,
  title={Convolutional pose machines},
  author={Wei, Shih-En and Ramakrishna, Varun and Kanade, Takeo and Sheikh, Yaser},
  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
  pages={4724--4732},
  year={2016}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
cpm 256x192 0.623 0.859 0.704 0.686 0.903 ckpt log
cpm 384x288 0.650 0.864 0.725 0.708 0.905 ckpt log

Topdown Heatmap + CPM on JHMDB

CPM (CVPR'2016)
@inproceedings{wei2016convolutional,
  title={Convolutional pose machines},
  author={Wei, Shih-En and Ramakrishna, Varun and Kanade, Takeo and Sheikh, Yaser},
  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
  pages={4724--4732},
  year={2016}
}
JHMDB (ICCV'2013)
@inproceedings{Jhuang:ICCV:2013,
  title = {Towards understanding action recognition},
  author = {H. Jhuang and J. Gall and S. Zuffi and C. Schmid and M. J. Black},
  booktitle = {International Conf. on Computer Vision (ICCV)},
  month = Dec,
  pages = {3192-3199},
  year = {2013}
}

Results on Sub-JHMDB dataset

The models are pre-trained on MPII dataset only. NO test-time augmentation (multi-scale /rotation testing) is used.

  • Normalized by Person Size

Split Arch Input Size Head Sho Elb Wri Hip Knee Ank Mean ckpt log
Sub1 cpm 368x368 96.1 91.9 81.0 78.9 96.6 90.8 87.3 89.5 ckpt log
Sub2 cpm 368x368 98.1 93.6 77.1 70.9 94.0 89.1 84.7 87.4 ckpt log
Sub3 cpm 368x368 97.9 94.9 87.3 84.0 98.6 94.4 86.2 92.4 ckpt log
Average cpm 368x368 97.4 93.5 81.5 77.9 96.4 91.4 86.1 89.8 - -
  • Normalized by Torso Size

Split Arch Input Size Head Sho Elb Wri Hip Knee Ank Mean ckpt log
Sub1 cpm 368x368 89.0 63.0 54.0 54.9 68.2 63.1 61.2 66.0 ckpt log
Sub2 cpm 368x368 90.3 57.9 46.8 44.3 60.8 58.2 62.4 61.1 ckpt log
Sub3 cpm 368x368 91.0 72.6 59.9 54.0 73.2 68.5 65.8 70.3 ckpt log
Average cpm 368x368 90.1 64.5 53.6 51.1 67.4 63.3 63.1 65.7 - -

Topdown Heatmap + CPM on Mpii

CPM (CVPR'2016)
@inproceedings{wei2016convolutional,
  title={Convolutional pose machines},
  author={Wei, Shih-En and Ramakrishna, Varun and Kanade, Takeo and Sheikh, Yaser},
  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
  pages={4724--4732},
  year={2016}
}
MPII (CVPR'2014)
@inproceedings{andriluka14cvpr,
  author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt},
  title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2014},
  month = {June}
}

Results on MPII val set

Arch Input Size Mean Mean@0.1 ckpt log
cpm 368x368 0.876 0.285 ckpt log



HRNet (CVPR’2019)


Topdown Heatmap + Hrnet on Animalpose

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
Animal-Pose (ICCV'2019)
@InProceedings{Cao_2019_ICCV,
    author = {Cao, Jinkun and Tang, Hongyang and Fang, Hao-Shu and Shen, Xiaoyong and Lu, Cewu and Tai, Yu-Wing},
    title = {Cross-Domain Adaptation for Animal Pose Estimation},
    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
    month = {October},
    year = {2019}
}

Results on AnimalPose validation set (1117 instances)

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hrnet_w32 256x256 0.736 0.959 0.832 0.775 0.966 ckpt log
pose_hrnet_w48 256x256 0.737 0.959 0.823 0.778 0.962 ckpt log

Topdown Heatmap + Hrnet on Ap10k

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
AP-10K (NeurIPS'2021)
@misc{yu2021ap10k,
      title={AP-10K: A Benchmark for Animal Pose Estimation in the Wild},
      author={Hang Yu and Yufei Xu and Jing Zhang and Wei Zhao and Ziyu Guan and Dacheng Tao},
      year={2021},
      eprint={2108.12617},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Results on AP-10K validation set

Arch Input Size AP AP50 AP75 APM APL ckpt log
pose_hrnet_w32 256x256 0.722 0.939 0.787 0.555 0.730 ckpt log
pose_hrnet_w48 256x256 0.731 0.937 0.804 0.574 0.738 ckpt log

Topdown Heatmap + Hrnet on Atrw

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
ATRW (ACM MM'2020)
@inproceedings{li2020atrw,
  title={ATRW: A Benchmark for Amur Tiger Re-identification in the Wild},
  author={Li, Shuyuan and Li, Jianguo and Tang, Hanlin and Qian, Rui and Lin, Weiyao},
  booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
  pages={2590--2598},
  year={2020}
}

Results on ATRW validation set

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hrnet_w32 256x256 0.912 0.973 0.959 0.938 0.985 ckpt log
pose_hrnet_w48 256x256 0.911 0.972 0.946 0.937 0.985 ckpt log

Topdown Heatmap + Hrnet on Horse10

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
Horse-10 (WACV'2021)
@inproceedings{mathis2021pretraining,
  title={Pretraining boosts out-of-domain robustness for pose estimation},
  author={Mathis, Alexander and Biasi, Thomas and Schneider, Steffen and Yuksekgonul, Mert and Rogers, Byron and Bethge, Matthias and Mathis, Mackenzie W},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={1859--1868},
  year={2021}
}

Results on Horse-10 test set

Set Arch Input Size PCK@0.3 NME ckpt log
split1 pose_hrnet_w32 256x256 0.951 0.122 ckpt log
split2 pose_hrnet_w32 256x256 0.949 0.116 ckpt log
split3 pose_hrnet_w32 256x256 0.939 0.153 ckpt log
split1 pose_hrnet_w48 256x256 0.973 0.095 ckpt log
split2 pose_hrnet_w48 256x256 0.969 0.101 ckpt log
split3 pose_hrnet_w48 256x256 0.961 0.128 ckpt log

Topdown Heatmap + Hrnet on Macaque

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
MacaquePose (bioRxiv'2020)
@article{labuguen2020macaquepose,
  title={MacaquePose: A novel ‘in the wild’macaque monkey pose dataset for markerless motion capture},
  author={Labuguen, Rollyn and Matsumoto, Jumpei and Negrete, Salvador and Nishimaru, Hiroshi and Nishijo, Hisao and Takada, Masahiko and Go, Yasuhiro and Inoue, Ken-ichi and Shibata, Tomohiro},
  journal={bioRxiv},
  year={2020},
  publisher={Cold Spring Harbor Laboratory}
}

Results on MacaquePose with ground-truth detection bounding boxes

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hrnet_w32 256x192 0.814 0.953 0.918 0.851 0.969 ckpt log
pose_hrnet_w48 256x192 0.818 0.963 0.917 0.855 0.971 ckpt log

Associative Embedding + Hrnet on Aic

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
AI Challenger (ArXiv'2017)
@article{wu2017ai,
  title={Ai challenger: A large-scale dataset for going deeper in image understanding},
  author={Wu, Jiahong and Zheng, He and Zhao, Bo and Li, Yixin and Yan, Baoming and Liang, Rui and Wang, Wenjia and Zhou, Shipei and Lin, Guosen and Fu, Yanwei and others},
  journal={arXiv preprint arXiv:1711.06475},
  year={2017}
}

Results on AIC validation set without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32 512x512 0.303 0.697 0.225 0.373 0.755 ckpt log

Results on AIC validation set with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32 512x512 0.318 0.717 0.246 0.379 0.764 ckpt log

Topdown Heatmap + Hrnet on Aic

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
AI Challenger (ArXiv'2017)
@article{wu2017ai,
  title={Ai challenger: A large-scale dataset for going deeper in image understanding},
  author={Wu, Jiahong and Zheng, He and Zhao, Bo and Li, Yixin and Yan, Baoming and Liang, Rui and Wang, Wenjia and Zhou, Shipei and Lin, Guosen and Fu, Yanwei and others},
  journal={arXiv preprint arXiv:1711.06475},
  year={2017}
}

Results on AIC val set with ground-truth bounding boxes

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hrnet_w32 256x192 0.323 0.762 0.219 0.366 0.789 ckpt log

Associative Embedding + Hrnet on Coco

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32 512x512 0.654 0.863 0.720 0.710 0.892 ckpt log
HRNet-w48 512x512 0.665 0.860 0.727 0.716 0.889 ckpt log

Results on COCO val2017 with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32 512x512 0.698 0.877 0.760 0.748 0.907 ckpt log
HRNet-w48 512x512 0.712 0.880 0.771 0.757 0.909 ckpt log

Associative Embedding + Hrnet + Udp on Coco

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
UDP (CVPR'2020)
@InProceedings{Huang_2020_CVPR,
  author = {Huang, Junjie and Zhu, Zheng and Guo, Feng and Huang, Guan},
  title = {The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32_udp 512x512 0.671 0.863 0.729 0.717 0.889 ckpt log
HRNet-w48_udp 512x512 0.681 0.872 0.741 0.725 0.892 ckpt log

Dekr + Hrnet on Coco

DEKR (CVPR'2021)
@inproceedings{geng2021bottom,
  title={Bottom-up human pose estimation via disentangled keypoint regression},
  author={Geng, Zigang and Sun, Ke and Xiao, Bin and Zhang, Zhaoxiang and Wang, Jingdong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14676--14686},
  year={2021}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32 512x512 0.680 0.868 0.745 0.728 0.897 ckpt log
HRNet-w48 640x640 0.709 0.876 0.773 0.758 0.909 ckpt log

Results on COCO val2017 with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt
HRNet-w32* 512x512 0.705 0.878 0.767 0.759 0.921 ckpt
HRNet-w48* 640x640 0.722 0.882 0.785 0.778 0.928 ckpt

* these configs are generally used for evaluation. The training settings are identical to their single-scale counterparts.

The results of models provided by the authors on COCO val2017 using the same evaluation protocol

Arch Input Size Setting AP AP50 AP75 AR AR50 ckpt
HRNet-w32 512x512 single-scale 0.678 0.868 0.744 0.728 0.897 see official implementation
HRNet-w48 640x640 single-scale 0.707 0.876 0.773 0.757 0.909 see official implementation
HRNet-w32 512x512 multi-scale 0.708 0.880 0.773 0.763 0.921 see official implementation
HRNet-w48 640x640 multi-scale 0.721 0.881 0.786 0.779 0.927 see official implementation

The discrepancy between these results and that shown in paper is attributed to the differences in implementation details in evaluation process.


Topdown Heatmap + Hrnet + Fp16 on Coco

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
FP16 (ArXiv'2017)
@article{micikevicius2017mixed,
  title={Mixed precision training},
  author={Micikevicius, Paulius and Narang, Sharan and Alben, Jonah and Diamos, Gregory and Elsen, Erich and Garcia, David and Ginsburg, Boris and Houston, Michael and Kuchaiev, Oleksii and Venkatesh, Ganesh and others},
  journal={arXiv preprint arXiv:1710.03740},
  year={2017}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hrnet_w32_fp16 256x192 0.746 0.905 0.88 0.800 0.943 ckpt log

Topdown Heatmap + Hrnet on Coco

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hrnet_w32 256x192 0.746 0.904 0.819 0.799 0.942 ckpt log
pose_hrnet_w32 384x288 0.760 0.906 0.829 0.810 0.943 ckpt log
pose_hrnet_w48 256x192 0.756 0.907 0.825 0.806 0.942 ckpt log
pose_hrnet_w48 384x288 0.767 0.910 0.831 0.816 0.946 ckpt log

Topdown Heatmap + Hrnet + Augmentation on Coco

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
Albumentations (Information'2020)
@article{buslaev2020albumentations,
  title={Albumentations: fast and flexible image augmentations},
  author={Buslaev, Alexander and Iglovikov, Vladimir I and Khvedchenya, Eugene and Parinov, Alex and Druzhinin, Mikhail and Kalinin, Alexandr A},
  journal={Information},
  volume={11},
  number={2},
  pages={125},
  year={2020},
  publisher={Multidisciplinary Digital Publishing Institute}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
coarsedropout 256x192 0.753 0.908 0.822 0.806 0.946 ckpt log
gridmask 256x192 0.752 0.906 0.825 0.804 0.943 ckpt log
photometric 256x192 0.753 0.909 0.825 0.805 0.943 ckpt log

Topdown Heatmap + Hrnet + Udp on Coco

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
UDP (CVPR'2020)
@InProceedings{Huang_2020_CVPR,
  author = {Huang, Junjie and Zhu, Zheng and Guo, Feng and Huang, Guan},
  title = {The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hrnet_w32_udp 256x192 0.760 0.907 0.827 0.811 0.945 ckpt log
pose_hrnet_w32_udp 384x288 0.769 0.908 0.833 0.817 0.944 ckpt log
pose_hrnet_w48_udp 256x192 0.767 0.906 0.834 0.817 0.945 ckpt log
pose_hrnet_w48_udp 384x288 0.772 0.910 0.835 0.820 0.945 ckpt log
pose_hrnet_w32_udp_regress 256x192 0.758 0.908 0.823 0.812 0.943 ckpt log

Note that, UDP also adopts the unbiased encoding/decoding algorithm of DARK.


Topdown Heatmap + Hrnet + Dark on Coco

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hrnet_w32_dark 256x192 0.757 0.907 0.823 0.808 0.943 ckpt log
pose_hrnet_w32_dark 384x288 0.766 0.907 0.831 0.815 0.943 ckpt log
pose_hrnet_w48_dark 256x192 0.764 0.907 0.830 0.814 0.943 ckpt log
pose_hrnet_w48_dark 384x288 0.772 0.910 0.836 0.820 0.946 ckpt log

Dekr + Hrnet on Crowdpose

DEKR (CVPR'2021)
@inproceedings{geng2021bottom,
  title={Bottom-up human pose estimation via disentangled keypoint regression},
  author={Geng, Zigang and Sun, Ke and Xiao, Bin and Zhang, Zhaoxiang and Wang, Jingdong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14676--14686},
  year={2021}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
CrowdPose (CVPR'2019)
@article{li2018crowdpose,
  title={CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark},
  author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},
  journal={arXiv preprint arXiv:1812.00324},
  year={2018}
}

Results on CrowdPose test without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w32 512x512 0.663 0.857 0.715 0.719 0.893 ckpt log
HRNet-w48 640x640 0.682 0.869 0.736 0.742 0.911 ckpt log

Results on CrowdPose test with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt
HRNet-w32* 512x512 0.692 0.874 0.748 0.755 0.926 ckpt
HRNet-w48* 640x640 0.696 0.869 0.749 0.769 0.933 ckpt

* these configs are generally used for evaluation. The training settings are identical to their single-scale counterparts.


Topdown Heatmap + Hrnet on Crowdpose

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
CrowdPose (CVPR'2019)
@article{li2018crowdpose,
  title={CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark},
  author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},
  journal={arXiv preprint arXiv:1812.00324},
  year={2018}
}

Results on CrowdPose test with YOLOv3 human detector

Arch Input Size AP AP50 AP75 AP (E) AP (M) AP (H) ckpt log
pose_hrnet_w32 256x192 0.675 0.825 0.729 0.770 0.687 0.553 ckpt log

Topdown Heatmap + Hrnet on H36m

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
Human3.6M (TPAMI'2014)
@article{h36m_pami,
  author = {Ionescu, Catalin and Papava, Dragos and Olaru, Vlad and Sminchisescu,  Cristian},
  title = {Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  publisher = {IEEE Computer Society},
  volume = {36},
  number = {7},
  pages = {1325-1339},
  month = {jul},
  year = {2014}
}

Results on Human3.6M test set with ground truth 2D detections

Arch Input Size EPE PCK ckpt log
pose_hrnet_w32 256x256 9.43 0.911 ckpt log
pose_hrnet_w48 256x256 7.36 0.932 ckpt log

Associative Embedding + Hrnet on MHP

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
MHP (ACM MM'2018)
@inproceedings{zhao2018understanding,
  title={Understanding humans in crowded scenes: Deep nested adversarial learning and a new benchmark for multi-human parsing},
  author={Zhao, Jian and Li, Jianshu and Cheng, Yu and Sim, Terence and Yan, Shuicheng and Feng, Jiashi},
  booktitle={Proceedings of the 26th ACM international conference on Multimedia},
  pages={792--800},
  year={2018}
}

Results on MHP v2.0 validation set without multi-scale test

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w48 512x512 0.583 0.895 0.666 0.656 0.931 ckpt log

Results on MHP v2.0 validation set with multi-scale test. 3 default scales ([2, 1, 0.5]) are used

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
HRNet-w48 512x512 0.592 0.898 0.673 0.664 0.932 ckpt log

Topdown Heatmap + Hrnet on Mpii

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
MPII (CVPR'2014)
@inproceedings{andriluka14cvpr,
  author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt},
  title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2014},
  month = {June}
}

Results on MPII val set

Arch Input Size Mean Mean@0.1 ckpt log
pose_hrnet_w32 256x256 0.900 0.334 ckpt log
pose_hrnet_w48 256x256 0.901 0.337 ckpt log

Topdown Heatmap + Hrnet + Dark on Mpii

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
MPII (CVPR'2014)
@inproceedings{andriluka14cvpr,
  author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt},
  title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2014},
  month = {June}
}

Results on MPII val set

Arch Input Size Mean Mean@0.1 ckpt log
pose_hrnet_w32_dark 256x256 0.904 0.354 ckpt log
pose_hrnet_w48_dark 256x256 0.905 0.360 ckpt log

Topdown Heatmap + Hrnet on Ochuman

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
OCHuman (CVPR'2019)
@inproceedings{zhang2019pose2seg,
  title={Pose2seg: Detection free human instance segmentation},
  author={Zhang, Song-Hai and Li, Ruilong and Dong, Xin and Rosin, Paul and Cai, Zixi and Han, Xi and Yang, Dingcheng and Huang, Haozhi and Hu, Shi-Min},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={889--898},
  year={2019}
}

Results on OCHuman test dataset with ground-truth bounding boxes

Following the common setting, the models are trained on COCO train dataset, and evaluate on OCHuman dataset.

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hrnet_w32 256x192 0.591 0.748 0.641 0.631 0.775 ckpt log
pose_hrnet_w32 384x288 0.606 0.748 0.650 0.647 0.776 ckpt log
pose_hrnet_w48 256x192 0.611 0.752 0.663 0.648 0.778 ckpt log
pose_hrnet_w48 384x288 0.616 0.749 0.663 0.653 0.773 ckpt log

Topdown Heatmap + Hrnet on Posetrack18

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
PoseTrack18 (CVPR'2018)
@inproceedings{andriluka2018posetrack,
  title={Posetrack: A benchmark for human pose estimation and tracking},
  author={Andriluka, Mykhaylo and Iqbal, Umar and Insafutdinov, Eldar and Pishchulin, Leonid and Milan, Anton and Gall, Juergen and Schiele, Bernt},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5167--5176},
  year={2018}
}

Results on PoseTrack2018 val with ground-truth bounding boxes

Arch Input Size Head Shou Elb Wri Hip Knee Ankl Total ckpt log
pose_hrnet_w32 256x192 87.4 88.6 84.3 78.5 79.7 81.8 78.8 83.0 ckpt log
pose_hrnet_w32 384x288 87.0 88.8 85.0 80.1 80.5 82.6 79.4 83.6 ckpt log
pose_hrnet_w48 256x192 88.2 90.1 85.8 80.8 80.7 83.3 80.3 84.4 ckpt log
pose_hrnet_w48 384x288 87.8 90.0 85.9 81.3 81.1 83.3 80.9 84.5 ckpt log

The models are first pre-trained on COCO dataset, and then fine-tuned on PoseTrack18.

Results on PoseTrack2018 val with MMDetection pre-trained Cascade R-CNN (X-101-64x4d-FPN) human detector

Arch Input Size Head Shou Elb Wri Hip Knee Ankl Total ckpt log
pose_hrnet_w32 256x192 78.0 82.9 79.5 73.8 76.9 76.6 70.2 76.9 ckpt log
pose_hrnet_w32 384x288 79.9 83.6 80.4 74.5 74.8 76.1 70.5 77.3 ckpt log
pose_hrnet_w48 256x192 80.1 83.4 80.6 74.8 74.3 76.8 70.4 77.4 ckpt log
pose_hrnet_w48 384x288 80.2 83.8 80.9 75.2 74.7 76.7 71.7 77.8 ckpt log

The models are first pre-trained on COCO dataset, and then fine-tuned on PoseTrack18.


Posewarper + Hrnet + Posetrack18 on Posetrack18

PoseWarper (NeurIPS'2019)
@inproceedings{NIPS2019_gberta,
title = {Learning Temporal Pose Estimation from Sparsely Labeled Videos},
author = {Bertasius, Gedas and Feichtenhofer, Christoph, and Tran, Du and Shi, Jianbo, and Torresani, Lorenzo},
booktitle = {Advances in Neural Information Processing Systems 33},
year = {2019},
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
PoseTrack18 (CVPR'2018)
@inproceedings{andriluka2018posetrack,
  title={Posetrack: A benchmark for human pose estimation and tracking},
  author={Andriluka, Mykhaylo and Iqbal, Umar and Insafutdinov, Eldar and Pishchulin, Leonid and Milan, Anton and Gall, Juergen and Schiele, Bernt},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5167--5176},
  year={2018}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

Note that the training of PoseWarper can be split into two stages.

The first-stage is trained with the pre-trained checkpoint on COCO dataset, and the main backbone is fine-tuned on PoseTrack18 in a single-frame setting.

The second-stage is trained with the last checkpoint from the first stage, and the warping offsets are learned in a multi-frame setting while the backbone is frozen.

Results on PoseTrack2018 val with ground-truth bounding boxes

Arch Input Size Head Shou Elb Wri Hip Knee Ankl Total ckpt log
pose_hrnet_w48 384x288 88.2 90.3 86.1 81.6 81.8 83.8 81.5 85.0 ckpt log

Results on PoseTrack2018 val with precomputed human bounding boxes from PoseWarper supplementary data files from this link1.

Arch Input Size Head Shou Elb Wri Hip Knee Ankl Total ckpt log
pose_hrnet_w48 384x288 81.8 85.6 82.7 77.2 76.8 79.0 74.4 79.8 ckpt log

1 Please download the precomputed human bounding boxes on PoseTrack2018 val from $PoseWarper_supp_files/posetrack18_precomputed_boxes/val_boxes.json and place it here: $mmpose/data/posetrack18/posetrack18_precomputed_boxes/val_boxes.json to be consistent with the config. Please refer to DATA Preparation for more detail about data preparation.


Associative Embedding + Hrnet on Coco-Wholebody

Associative Embedding (NIPS'2017)
@inproceedings{newell2017associative,
  title={Associative embedding: End-to-end learning for joint detection and grouping},
  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
  booktitle={Advances in neural information processing systems},
  pages={2277--2287},
  year={2017}
}
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
COCO-WholeBody (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody v1.0 val without multi-scale test

Arch Input Size Body AP Body AR Foot AP Foot AR Face AP Face AR Hand AP Hand AR Whole AP Whole AR ckpt log
HRNet-w32+ 512x512 0.551 0.650 0.271 0.451 0.564 0.618 0.159 0.238 0.342 0.453 ckpt log
HRNet-w48+ 512x512 0.592 0.686 0.443 0.595 0.619 0.674 0.347 0.438 0.422 0.532 ckpt log

Note: + means the model is first pre-trained on original COCO dataset, and then fine-tuned on COCO-WholeBody dataset. We find this will lead to better performance.


Topdown Heatmap + Hrnet on Coco-Wholebody

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
COCO-WholeBody (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Results on COCO-WholeBody v1.0 val with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size Body AP Body AR Foot AP Foot AR Face AP Face AR Hand AP Hand AR Whole AP Whole AR ckpt log
pose_hrnet_w32 256x192 0.700 0.746 0.567 0.645 0.637 0.688 0.473 0.546 0.553 0.626 ckpt log
pose_hrnet_w32 384x288 0.701 0.773 0.586 0.692 0.727 0.783 0.516 0.604 0.586 0.674 ckpt log
pose_hrnet_w48 256x192 0.700 0.776 0.672 0.785 0.656 0.743 0.534 0.639 0.579 0.681 ckpt log
pose_hrnet_w48 384x288 0.722 0.790 0.694 0.799 0.777 0.834 0.587 0.679 0.631 0.716 ckpt log

Topdown Heatmap + Hrnet + Dark on Coco-Wholebody

HRNet (CVPR'2019)
@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}
DarkPose (CVPR'2020)
@inproceedings{zhang2020distribution,
  title={Distribution-aware coordinate representation for human pose estimation},
  author={Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7093--7102},
  year={2020}
}
COCO-WholeBody (ECCV'2020)
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},