AdaptivePose: Human Parts as Adaptive Points

• URL: http://arxiv.org/abs/2112.13635v1
• Date: Mon, 27 Dec 2021 12:46:12 GMT
• Title: AdaptivePose: Human Parts as Adaptive Points
• Authors: Yabo Xiao, Xiaojuan Wang, Dongdong Yu, Guoli Wang, Qian Zhang, Mingshu He
• Abstract summary: We propose to represent the human parts as points and present a novel body representation. 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 test-dev dataset.
• Score: 7.833251195123861
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-person pose estimation methods generally follow top-down 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 human-part 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.

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