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Integral Human Pose Regression

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arxiv 1711.08229 v4 pith:R5YTWOZG submitted 2017-11-22 cs.CV

Xiao Sun , Bin Xiao , Fangyin Wei , Shuang Liang , Yichen Wei This is my paper
classification cs.CV
keywords heatposerepresentationdifferentiableestimationhumanintegralissues
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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.

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Cited by 1 Pith paper

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  1. 2D Pre-Training for 3D Pose Estimation

    cs.CV 2026-04 unverdicted novelty 3.0 of 10

    2D pre-training for 3D human pose estimation yields lower error and higher efficiency than 3D-only training, reaching MPJPE below 64.5 mm on standard benchmarks.

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