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 |