Awesome
token-Pruned Pose Transformer
"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
Introduction
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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.
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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.
Running
For monocular 2D pose estimation, please see single-view-PPT.
For multi-view 3D pose estimation, please see multi-view-PPT.
Citation
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}
}