Relation-Based Associative Joint Location for Human Pose Estimation in Videos
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Video-based human pose estimation (HPE) is a vital yet challenging task. While deep learning methods have made significant progress for the HPE, most approaches to this task detect each joint independently, damaging the pose structural information. In this paper, unlike the prior methods, we propose a Relation-based Pose Semantics Transfer Network (RPSTN) to locate joints associatively. Specifically, we design a lightweight joint relation extractor (JRE) to model the pose structural features and associatively generate heatmaps for joints by modeling the relation between any two joints heuristically instead of building each joint heatmap independently. Actually, the proposed JRE module models the spatial configuration of human poses through the relationship between any two joints. Moreover, considering the temporal semantic continuity of videos, the pose semantic information in the current frame is beneficial for guiding the location of joints in the next frame. Therefore, we use the idea of knowledge reuse to propagate the pose semantic information between consecutive frames. In this way, the proposed RPSTN captures temporal dynamics of poses. On the one hand, the JRE module can infer invisible joints according to the relationship between the invisible joints and other visible joints in space. On the other hand, in the time, the propose model can transfer the pose semantic features from the non-occluded frame to the occluded frame to locate occluded joints. Therefore, our method is robust to the occlusion and achieves state-of-the-art results on the two challenging datasets, which demonstrates its effectiveness for video-based human pose estimation. We will release the code and models publicly.
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