Distribution-Aware Coordinate Representation for Human Pose Estimation

CVPR 2020  ·  Feng Zhang, Xiatian Zhu, Hanbin Dai, Mao Ye, Ce Zhu ·

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. read more

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Results from the Paper

 Ranked #1 on Multi-Person Pose Estimation on COCO (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Multi-Person Pose Estimation COCO DarkPose AP 0.774 # 1
Keypoint Detection COCO DarkPose Test AP 76.2 # 3
Pose Estimation COCO test-dev HRNet-W48+DARK AP 77.4 # 1
AP50 92.6 # 4
AP75 84.6 # 1
APL 83.7 # 1
APM 73.6 # 2
AR 82.3 # 1