-
Integral Human Pose Regression
AbstractState-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 non-differentiable post-processing 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.
Related Material
[pdf] [arXiv][bibtex]@InProceedings{Sun_2018_ECCV,
author = {Sun, Xiao and Xiao, Bin and Wei, Fangyin and Liang, Shuang and Wei, Yichen},
title = {Integral Human Pose Regression},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}
ECCV 2018 papers
The ECCV 2018 papers, provided here by the Computer Vision Foundation, are the author-created versions. The content of the papers is identical to the content of the officially published ECCV 2018 LNCS version of the papers as available on SpringerLink: https://link.springer.com/conference/eccv.
Intellectual property rights, copyright and all rights therein are retained by authors, by Springer as the publisher of the official ECCV 2018 proceedings or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.