{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/differentiable-hierarchical-graph-grouping","title":"Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation","arxiv_id":"2007.11864","date":"2020-07-23","proceeding":"ECCV 2020 8","authors":["Sheng Jin","Wentao Liu","Enze Xie","Wenhai Wang","Chen Qian","Wanli Ouyang","Ping Luo"],"abstract":"Multi-person pose estimation is challenging because it localizes body keypoints for multiple persons simultaneously. Previous methods can be divided into two streams, i.e. top-down and bottom-up methods. The top-down methods localize keypoints after human detection, while the bottom-up methods localize keypoints directly and then cluster/group them for different persons, which are generally more efficient than top-down methods. However, in existing bottom-up methods, the keypoint grouping is usually solved independently from keypoint detection, making them not end-to-end trainable and have sub-optimal performance. In this paper, we investigate a new perspective of human part grouping and reformulate it as a graph clustering task. Especially, we propose a novel differentiable Hierarchical Graph Grouping (HGG) method to learn the graph grouping in bottom-up multi-person pose estimation task. Moreover, HGG is easily embedded into main-stream bottom-up methods. It takes human keypoint candidates as graph nodes and clusters keypoints in a multi-layer graph neural network model. The modules of HGG can be trained end-to-end with the keypoint detection network and is able to supervise the grouping process in a hierarchical manner. To improve the discrimination of the clustering, we add a set of edge discriminators and macro-node discriminators. Extensive experiments on both COCO and OCHuman datasets demonstrate that the proposed method improves the performance of bottom-up pose estimation methods.","url_abs":"https://arxiv.org/abs/2007.11864v1","url_pdf":"https://arxiv.org/pdf/2007.11864v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"2d-human-pose-estimation","task_name":"2D Human Pose Estimation"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"graph-clustering","task_name":"Graph Clustering"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"human-detection","task_name":"Human Detection"},{"task_slug":"keypoint-detection","task_name":"Keypoint Detection"},{"task_slug":"multi-person-pose-estimation","task_name":"Multi-Person Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/2d-human-pose-estimation-on-ochuman","task":"2D Human Pose Estimation","dataset":"OCHuman","model":"HGG (AE+)","rank_in_archive_order":5,"of":11,"metrics":{"Test AP":"36.0","Validation AP":"41.8"},"uses_additional_data":false},{"leaderboard":"/sota/keypoint-detection-on-ochuman","task":"Keypoint Detection","dataset":"OCHuman","model":"HGG (AE+)","rank_in_archive_order":4,"of":10,"metrics":{"Test AP":"36.0","Validation AP":"41.8"},"uses_additional_data":false},{"leaderboard":"/sota/pose-estimation-on-ochuman","task":"Pose Estimation","dataset":"OCHuman","model":"HGG (AE+)","rank_in_archive_order":13,"of":19,"metrics":{"Test AP":"36.0","Validation AP":"41.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2007.11864","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}