Abstract | ||
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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. |
Year | Venue | Keywords |
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2022 | AAAI Conference on Artificial Intelligence | Computer Vision (CV) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yabo Xiao | 1 | 0 | 0.68 |
Xiaojuan Wang | 2 | 0 | 0.68 |
Dongdong Yu | 3 | 63 | 7.07 |
Guoli Wang | 4 | 221 | 21.26 |
Qian Zhang | 5 | 17 | 21.63 |
Mingshu He | 6 | 0 | 0.68 |