Body(3D,Kpt,Vid)

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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.

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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