Human pose estimation deeply relies on visual clues and constraint clues 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).
The contributions of this work are summarized as follows:
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We propose to use tokens to represent each keypoint entity. In this way, visual cue learning and constraint cue learning are explicitly incorporated into a unified framework.
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Both hybrid and pure Transformer-based architectures are explored in this work. To the best of our knowledge, our proposed TokenPose-T is the first pure Transformer-based model for 2D human pose estimation.
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We conduct experiments over two widely-used benchmark datasets: COCO keypoint detection dataset and MPII Human Pose dataset. TokenPose achieves competitive state-of-the-art performance with much fewer parameters and computation cost compared with existing CNN-based counterparts.