{"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/mhentropy-entropy-meets-multiple-hypotheses","title":"MHEntropy: Entropy Meets Multiple Hypotheses for Pose and Shape Recovery","arxiv_id":null,"date":"2023-01-01","proceeding":"ICCV 2023 1","authors":["Rongyu Chen","Linlin Yang","Angela Yao"],"abstract":"    For monocular RGB-based 3D pose and shape estimation, multiple solutions are often feasible due to factors like occlusion and truncation. This work presents a multi-hypothesis probabilistic framework by optimizing the Kullback-Leibler divergence (KLD) between the data and model distribution. Our formulation reveals a connection between the pose entropy and diversity in the multiple hypotheses that has been neglected by previous works. For a comprehensive evaluation, besides the best hypothesis (BH) metric, we factor in visibility for evaluating diversity. Additionally, our framework is label-friendly, in that it can be learned from only partial 2D keypoints, e.g., those that are visible. Experiments on both ambiguous and real-world benchmarks demonstrate that our method outperforms other state-of-the-art multi-hypothesis methods in a comprehensive evaluation. The project page is at https://gloryyrolg.github.io/MHEntropy.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2023/html/Chen_MHEntropy_Entropy_Meets_Multiple_Hypotheses_for_Pose_and_Shape_Recovery_ICCV_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2023/papers/Chen_MHEntropy_Entropy_Meets_Multiple_Hypotheses_for_Pose_and_Shape_Recovery_ICCV_2023_paper.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":[{"paper_slug":"mhentropy-entropy-meets-multiple-hypotheses","repo_url":"https://github.com/GloryyrolG/MHEntropy","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-hand-pose-estimation","task_name":"3D Hand Pose Estimation"},{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"3d-human-reconstruction","task_name":"3D Human Reconstruction"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"human-mesh-recovery","task_name":"Human Mesh Recovery"},{"task_slug":"multi-hypotheses-3d-human-pose-estimation","task_name":"Multi-Hypotheses 3D Human Pose Estimation"}],"methods":[{"method_slug":"normalizing-flows","method_name":"Normalizing Flows"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-hypotheses-3d-human-pose-estimation-on-2","task":"Multi-Hypotheses 3D Human Pose Estimation","dataset":"AH36M","model":"MHEntropy (3D)","rank_in_archive_order":1,"of":10,"metrics":{"Best-Hypothesis MPJPE (n = 25)":"-","Best-Hypothesis PMPJPE (n = 25)":"50.6","H36M PMPJPE (n = 1)":"-","H36M PMPJPE (n = 25)":"36.8","Most-Likely Hypothesis PMPJPE (n = 1)":"-"},"uses_additional_data":false},{"leaderboard":"/sota/multi-hypotheses-3d-human-pose-estimation-on-2","task":"Multi-Hypotheses 3D Human Pose Estimation","dataset":"AH36M","model":"MHEntropy (2D Vis)","rank_in_archive_order":4,"of":10,"metrics":{"Best-Hypothesis MPJPE (n = 25)":"-","Best-Hypothesis PMPJPE (n = 25)":"66.4","H36M PMPJPE (n = 1)":"-","H36M PMPJPE (n = 25)":"51.3","Most-Likely Hypothesis PMPJPE (n = 1)":"-"},"uses_additional_data":false},{"leaderboard":"/sota/multi-hypotheses-3d-human-pose-estimation-on","task":"Multi-Hypotheses 3D Human Pose Estimation","dataset":"Human3.6M","model":"MHEntropy","rank_in_archive_order":12,"of":12,"metrics":{"Average PMPJPE (mm)":"36.8"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}