TokenPose: Learning Keypoint Tokens for Human Pose Estimation

Human pose estimation deeply relies on visual clues and anatomical constraints between parts to locate keypoints. Most existing CNN-based methods do well in visual representation, however, lacking in the ability to explicitly learn the constraint relationships between keypoints. In this paper, we propose a novel approach based on Token representation for human Pose estimation (TokenPose). In detail, each keypoint is explicitly embedded as a token to simultaneously learn constraint relationships and appearance cues from images. Extensive experiments show that the small and large TokenPose models are on par with state-of-the-art CNN-based counterparts while being more lightweight. Specifically, our TokenPose-S and TokenPose-L achieve 72.5 AP and 75.8 AP on COCO validation dataset respectively, with significant reduction in parameters (down arrow 80.6%; down arrow 56.8%) and GFLOPs (down arrow 75.3%; down arrow 24.7%). Code is publicly available(1).

Automated summary

This paper introduces a novel approach for human pose estimation based on Token representation, which can learn constraint relationships and appearance cues simultaneously, achieving comparable performance with existing methods in experiments.