Hierarchical graph neural network for human pose estimation

Guanghua Zheng, Zhongqiu Zhao, Yi Yang, Zhao Zhang

Abstract: The human bodies are hierarchical structure and human pose estimation is highly dependent on the correlation between the keypoints of human body. Most existing CNN-based and Transformer-based methods perform well in the visual representation but lack the ability to learn the correlation between keypoints explicitly. To address this problem, we propose a human pose estimation method based on a hierarchical graph neural network with dynamic keypoint weight. The hierarchical graph neural network consists of three parts: the inter-layer process, hierarchical process, and iterative inference. The inter-layer process and hierarchical process learn the correlation between similar keypoints and further keypoints, respectively. And iterative inference is used to refine and aggregate more useful information between keypoints. Furthermore, the dynamic keypoint weight network gives each keypoint a different degree of importance. Extensive experiments demonstrate that our HGNPose-M and HGNPose-L achieve 76.5 AP(↑2.1) and 77.4 AP(↑1.6) on the COCO dataset respectively.