Algorithms

---

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

---

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.

---

Associative Embedding (NIPS’2017)

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 + 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 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 + 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 + 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 + 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 + 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 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 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 + 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.

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.

---

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 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}
}

HRNet (CVPR'2019)

@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 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 + 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 + 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 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 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 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_hrnetv2_w18_udp	256x256	0.992	0.902	2.21	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	0.457	0.578	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

---

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 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 + 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 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 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

---

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