Human Pose Estimation with Shape Aware Loss

Abstract

Although the mean square error (mse) of heatmap is an intuitive loss for heatmap-based human pose estimation, the joints localization accuracy may not be improved when heatmap mse reduces. In this paper, we show that a great cause for such misalignment is the unnecessary requirement from heatmap mse on the irrelevant Gaussian parameter, i.e. maximum. The coordinate prediction is precise as long as the probability distribution held by the predicted heatmap is a well-shaped Gaussian distribution and has the same center as the ground truth. However, heatmap mse unnecessarily requires the Gaussian distribution to hold the same maximum as the ground truth. Correspondingly, we introduce mse on the image gradients of the target and predicted heatmap (referred to as gradmap mse) to focus on the shape of the heatmap. Combining heatmap and gradmap mse, we propose a simple yet effective Shape Aware Loss (SAL) method. Being model-agnostic, our method can benefit various existing models. We apply SAL to the three latest network architectures and obtain performance improvements for all of them. Comparisons of the visualized predicted heatmaps further prove the effectiveness of the proposed method.