Semantic-Structural Graph Convolutional Networks for Whole-Body Human Pose Estimation

Existing whole-body human pose estimation methods mostly segment the parts of the body's hands and feet for specific processing, which not only splits the overall semantics of the body, but also increases the amount of calculation and the complexity of the model. To address these drawbacks, we designed a novel semantic-structural graph convolutional network (SSGCN) for whole-body human pose estimation tasks, which leverages the whole-body graph structure to analyze the semantics of the whole-body keypoints through a graph convolutional network and improves the accuracy of pose estimation. Firstly, we introduced a novel heat-map-based keypoint embedding, which encodes the position information and feature information of the keypoints of the human body. Secondly, we propose a novel semantic-structural graph convolutional network consisting of several sets of cascaded structure-based graph layers and data-dependent whole-body non-local layers. Specifically, the proposed method extracts groups of keypoints and constructs a high-level abstract body graph to process the high-level semantic information of the whole-body keypoints. The experimental results showed that our method achieved very promising results on the challenging COCO whole-body dataset.