  In this paper, we focus on the coordinate representation in human pose
estimation. While being the standard choice, heatmap based representation has
not been systematically investigated. We found that the process of coordinate
decoding (i.e. transforming the predicted heatmaps to the coordinates) 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 and propose a 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 them together, we formulate a
novel Distribution-Aware coordinate Representation for 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 COCO keypoint
detection challenge, validating the usefulness and effectiveness of our novel
coordinate representation idea. The project page containing more details is at
https://ilovepose.github.io/coco
