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For tackling the task of 2D human pose estimation, the great majority of therecent methods regard this task as a heatmap estimation problem, and optimizethe heatmap prediction using the Gaussian-smoothed heatmap as the optimizationobjective and using the pixel-wise loss (e.g. MSE) as the loss function. Inthis paper, we show that optimizing the heatmap prediction in such a way, themodel performance of body joint localization, which is the intrinsic objectiveof this task, may not be consistently improved during the optimization processof the heatmap prediction. To address this problem, from a novel perspective,we propose to formulate the optimization of the heatmap prediction as adistribution matching problem between the predicted heatmap and the dotannotation of the body joint directly. By doing so, our proposed method doesnot need to construct the Gaussian-smoothed heatmap and can achieve a moreconsistent model performance improvement during the optimization of the heatmapprediction. We show the effectiveness of our proposed method through extensiveexperiments on the COCO dataset and the MPII dataset.

Heatmap Distribution Matching for Human Pose Estimation