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Body(3D,Kpt,Vid)




H36m Dataset


Video Pose Lift + Videopose3d on H36m

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

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

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

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

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

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

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

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

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

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




Mpi_inf_3dhp Dataset


Video Pose Lift + Videopose3d on Mpi_inf_3dhp

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

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

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