Part2Pose: Inferring Human Pose From Parts in Complex Scenes

Abstract

Most of existing Human Pose Estimation (HPE) methods struggle to handle with challenges such as changeable poses, complex backgrounds, and occlusion encountered in complex scenes. To address these problems, a novel HPE network, called Part2Pose, is proposed in this paper. In our Part2Pose, instead of focusing on small-sized keypoints like existing HPE methods do, we first extract image features based on human body parts to expand the detection scope. This strategy enhances the robustness of the extracted features to variations and distractions in complex scenes. Then, a Transformer-based Global Part Relation Module (GPRM) and a graph convolutional network-based Local Part Relation Module (LPRM) are used to capture global and local relationships among different body parts to help infer the position of keypoints. Extensive experiments on challenging datasets, including COCO, CrowdPose and OCHuman, show that the proposed Part2Pose can surpass existing popular state-of-the-art HPE methods. The combination with lightweight networks confirms the robustness and generalizability of our Part2Pose.