| # Integral Human Pose Regression | |
| <!-- [ALGORITHM] --> | |
| <details> | |
| <summary align="right"><a href="https://arxiv.org/abs/1711.08229">IPR (ECCV'2018)</a></summary> | |
| ```bibtex | |
| @inproceedings{sun2018integral, | |
| title={Integral human pose regression}, | |
| author={Sun, Xiao and Xiao, Bin and Wei, Fangyin and Liang, Shuang and Wei, Yichen}, | |
| booktitle={Proceedings of the European conference on computer vision (ECCV)}, | |
| pages={529--545}, | |
| year={2018} | |
| } | |
| ``` | |
| </details> | |
| ## Abstract | |
| <!-- [ABSTRACT] --> | |
| State-of-the-art human pose estimation methods are based on heat map representation. In spite of the good performance, the representation has a few issues in nature, such as not differentiable and quantization error. This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues. It is differentiable, efficient, and compatible with any heat map based methods. Its effectiveness is convincingly validated via comprehensive ablation experiments under various settings, specifically on 3D pose estimation, for the first time. | |
| <!-- [IMAGE] --> | |
| <div align=center> | |
| <img src="https://user-images.githubusercontent.com/13503330/189809459-026c7a9b-2260-4676-bc64-de92ce8784ce.png"> | |
| </div> | |