STN-enhanced message passing guided by adversarial learning for human pose estimation

Abstract

In this paper, an STN (Spatial Transformer Network)-enhanced message passing module guided by adversarial learning is proposed for human pose estimation. Transformations performed on the predicted heatmaps mean to modify the raw predictions and conduct the message passing among human joints in an elegant way. Firstly, we employ STN submodule to transform the predicted heatmap of one human joint to heatmap of its neighboring joint to remove ambiguity of its neighboring predictions. STN submodule automatically learns the geometric information between adjacent joints and thus builds related neighboring associations among them. Nevertheless, it seems difficult for STN submodule to learn inherent geometric information from a single RGB image alone. Secondly, limb guidance is introduced to assist STN in predicting corresponding correlations. Since quality of limb predictions poses great significance for the guidance, we propose to exploit adversarial learning to improve the quality of limb heatmaps which are easier to learn than precise keypoint location. Hence, the precision of STN transformation improves owing to more precise prior instructions. However, STN submodule might be confused when performing the transformation due to massive noises of the heatmaps. To circumvent this dilemma, at last, we propose to utilize Weighted Mean Square Error (WMSE) loss and convolutional random walk (CRW) which improve the performance further. Our method achieves competitive results on both MPII and LSP benchmarks.