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.
Integral Human Pose Regression
Xiao Sun,Bin Xiao,Shuang Liang,Yichen Wei
Published 2017 in European Conference on Computer Vision
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- Publication year
2017
- Venue
European Conference on Computer Vision
- Publication date
2017-11-22
- Fields of study
Computer Science
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