While being the de facto standard coordinate representation in human pose
estimation, heatmap is never systematically investigated in the literature, to
our best knowledge. This work fills this gap by studying the coordinate
representation with a particular focus on the heatmap. Interestingly, we found
that the process of decoding the predicted heatmaps into the final joint
coordinates in the original image space is surprisingly significant for human
pose estimation performance, which nevertheless was not recognised before. In
light of the discovered importance, we further probe the design limitations of
the standard coordinate decoding method widely used by existing methods, and
propose a more principled distribution-aware decoding method. Meanwhile, we
improve the standard coordinate encoding process (i.e. transforming
ground-truth coordinates to heatmaps) by generating accurate heatmap
distributions for unbiased model training. Taking the two together, we
formulate a novel Distribution-Aware coordinate Representation of Keypoint
(DARK) method. Serving as a model-agnostic plug-in, DARK significantly improves
the performance of a variety of state-of-the-art human pose estimation models.
Extensive experiments show that DARK yields the best results on two common
benchmarks, MPII and COCO, consistently validating the usefulness and
effectiveness of our novel coordinate representation idea.

本文研究基于 heatmap 的人体姿态估计中的坐标表示方法，并提出了一种更为合理和有效的分布感知解码方法和准确的 heatmap 分布生成方法，最终提出了一种名为 DARK 的新型坐标表示方法，并在 MPII 和 COCO 等基准测试上获得最佳结果，提高了各种最新人体姿态估计模型的性能。