  We propose a human pose estimation framework that solves the task in the
regression-based fashion. Unlike previous regression-based methods, which often
fall behind those state-of-the-art methods, we formulate the pose estimation
task into a sequence prediction problem that can effectively be solved by
transformers. Our framework is simple and direct, bypassing the drawbacks of
the heatmap-based pose estimation. Moreover, with the attention mechanism in
transformers, our proposed framework is able to adaptively attend to the
features most relevant to the target keypoints, which largely overcomes the
feature misalignment issue of previous regression-based methods and
considerably improves the performance. Importantly, our framework can
inherently take advantages of the structured relationship between keypoints.
Experiments on the MS-COCO and MPII datasets demonstrate that our method can
significantly improve the state-of-the-art of regression-based pose estimation
and perform comparably with the best heatmap-based pose estimation methods.
