"PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation" Haoyu Ma, Zhe Wang, Yifei Chen, Deying Kong, Liangjian Chen, Xingwei Liu, Xiangyi Yan, Hao Tang, and Xiaohui Xie.
In ECCV 2022

paper

• We propose the token-Pruned Pose Transformer (PPT) for efficient 2D human pose estimation, which can locate the human body area and prune background tokens with the help of a Human Token Identification module.

• We propose the strategy of "Human area fusion" for multi-view pose estimation. Built upon PPT, the multi-view PPT can efficiently fuse cues from human areas of multiple views.

For monocular 2D pose estimation, please see single-view-PPT.
For multi-view 3D pose estimation, please see multi-view-PPT.

If you find our code helps your research, please cite the paper:

@inproceedings{ma2022ppt,
  title={PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation},
  author={Ma, Haoyu and Wang, Zhe and Chen, Yifei and Kong, Deying and Chen, Liangjian and Liu, Xingwei and Yan, Xiangyi and Tang, Hao and Xie, Xiaohui},
  booktitle={ECCV},
  year={2022}
}

• HRNet

• TransPose

• TokenPose

• Cross-view Fusion

• Epipolar Transformer

• TransFusion