  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.
