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In this paper, we focus on the coordinate representation in human poseestimation. While being the standard choice, heatmap based representation hasnot been systematically investigated. We found that the process of coordinatedecoding (i.e. transforming the predicted heatmaps to the coordinates) issurprisingly significant for human pose estimation performance, whichnevertheless was not recognised before. In light of the discovered importance,we further probe the design limitations of the standard coordinate decodingmethod and propose a principled distribution-aware decoding method. Meanwhile,we improve the standard coordinate encoding process (i.e. transformingground-truth coordinates to heatmaps) by generating accurate heatmapdistributions for unbiased model training. Taking them together, we formulate anovel Distribution-Aware coordinate Representation for Keypoint (DARK) method.Serving as a model-agnostic plug-in, DARK significantly improves theperformance of a variety of state-of-the-art human pose estimation models.Extensive experiments show that DARK yields the best results on COCO keypointdetection challenge, validating the usefulness and effectiveness of our novelcoordinate representation idea. The project page containing more details is athttps://ilovepose.github.io/coco

Joint COCO and Mapillary Workshop at ICCV 2019 Keypoint Detection  Challenge Track Technical Report: Distribution-Aware Coordinate  Representation for Human Pose Estimation