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arXiv · 1901.00148

Rethinking on Multi-Stage Networks for Human Pose Estimation

Wenbo Li·Zhicheng Wang·Binyi Yin·Qixiang Peng·Yuming Du·Tianzi Xiao·Gang Yu·Hongtao Lu·Yichen Wei·Jian Sun
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Abstract

Existing pose estimation approaches fall into two categories: single-stage and multi-stage methods. While multi-stage methods are seemingly more suited for the task, their performance in current practice is not as good as single-stage methods. This work studies this issue. We argue that the current multi-stage methods' unsatisfactory performance comes from the insufficiency in various design choices. We propose several improvements, including the single-stage module design, cross stage feature aggregation, and coarse-to-fine supervision. The resulting method establishes the new state-of-the-art on both MS COCO and MPII Human Pose dataset, justifying the effectiveness of a multi-stage architecture. The source code is publicly available for further research.

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Wenbo Li, Zhicheng Wang, Binyi Yin, Qixiang Peng, Yuming Du, Tianzi Xiao, Gang Yu, Hongtao Lu, Yichen Wei, Jian Sun. 2019-05-30. Rethinking on Multi-Stage Networks for Human Pose Estimation. https://arxiv.org/abs/1901.00148

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