AdaptivePose: Human Parts as Adaptive Points

Multi-person pose estimation methods generally follow topdown and bottom-up paradigms, both of which can be considered as two-stage approaches thus leading to the high computation cost and low efficiency. Towards a compact and efficient pipeline for multi-person pose estimation task, in this paper, we propose to represent the human parts as points and present a novel body representation, which leverages an adaptive point set including the human center and seven humanpart related points to represent the human instance in a more fine-grained manner. The novel representation is more capable of capturing the various pose deformation and adaptively factorizes the long-range center-to-joint displacement thus delivers a single-stage differentiable network to more precisely regress multi-person pose, termed as AdaptivePose. For inference, our proposed network eliminates the grouping as well as refinements and only needs a single-step disentangling process to form multi-person pose. Without any bells and whistles, we achieve the best speed-accuracy trade-offs of 67. 4% AP / 29. 4 fps with DLA-34 and 71. 3% AP / 9. 1 fps with HRNet-W48 on COCO test-dev dataset.

Authors

• Yabo Xiao
• Xiao Juan Wang
• Dongdong Yu AIsphere Inc.
• Guoli Wang Horizon Robotics
• Qian Zhang Horizon Robotics
• Mingshu HE

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