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arXiv · 1704.00159

Compositional Human Pose Regression

Xiao Sun·Jiaxiang Shang·Shuang Liang·Yichen Wei
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Abstract

Regression based methods are not performing as well as detection based methods for human pose estimation. A central problem is that the structural information in the pose is not well exploited in the previous regression methods. In this work, we propose a structure-aware regression approach. It adopts a reparameterized pose representation using bones instead of joints. It exploits the joint connection structure to define a compositional loss function that encodes the long range interactions in the pose. It is simple, effective, and general for both 2D and 3D pose estimation in a unified setting. Comprehensive evaluation validates the effectiveness of our approach. It significantly advances the state-of-the-art on Human3.6M and is competitive with state-of-the-art results on MPII.

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Xiao Sun, Jiaxiang Shang, Shuang Liang, Yichen Wei. 2017-08-02. Compositional Human Pose Regression. https://arxiv.org/abs/1704.00159

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