  Multi-person pose estimation generally follows top-down and bottom-up
paradigms. Both of them use an extra stage ($\boldsymbol{e.g.,}$ human
detection in top-down paradigm or grouping process in bottom-up paradigm) to
build the relationship between the human instance and corresponding keypoints,
thus leading to the high computation cost and redundant two-stage pipeline. To
address the above issue, we propose to represent the human parts as adaptive
points and introduce a fine-grained body representation method. The novel body
representation is able to sufficiently encode the diverse pose information and
effectively model the relationship between the human instance and corresponding
keypoints in a single-forward pass. With the proposed body representation, we
further deliver a compact single-stage multi-person pose regression network,
termed as AdaptivePose. During inference, our proposed network only needs a
single-step decode operation to form the multi-person pose without complex
post-processes and refinements. We employ AdaptivePose for both 2D/3D
multi-person pose estimation tasks to verify the effectiveness of AdaptivePose.
Without any bells and whistles, we achieve the most competitive performance on
MS COCO and CrowdPose in terms of accuracy and speed. Furthermore, the
outstanding performance on MuCo-3DHP and MuPoTS-3D further demonstrates the
effectiveness and generalizability on 3D scenes. Code is available at
https://github.com/buptxyb666/AdaptivePose.
