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 |