{"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/h2onet-hand-occlusion-and-orientation-aware","title":"H2ONet: Hand-Occlusion-and-Orientation-Aware Network for Real-Time 3D Hand Mesh Reconstruction","arxiv_id":null,"date":"2023-01-01","proceeding":"CVPR 2023 1","authors":["Hao Xu","Tianyu Wang","Xiao Tang","Chi-Wing Fu"],"abstract":"    Real-time 3D hand mesh reconstruction is challenging, especially when the hand is holding some object. Beyond the previous methods, we design H2ONet to fully exploit non-occluded information from multiple frames to boost the reconstruction quality. First, we decouple hand mesh reconstruction into two branches, one to exploit finger-level non-occluded information and the other to exploit global hand orientation, with lightweight structures to promote real-time inference. Second, we propose finger-level occlusion-aware feature fusion, leveraging predicted finger-level occlusion information as guidance to fuse finger-level information across time frames. Further, we design hand-level occlusion-aware feature fusion to fetch non-occluded information from nearby time frames. We conduct experiments on the Dex-YCB and HO3D-v2 datasets with challenging hand-object occlusion cases, manifesting that H2ONet is able to run in real-time and achieves state-of-the-art performance on both the hand mesh and pose precision. The code will be released on GitHub.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2023/html/Xu_H2ONet_Hand-Occlusion-and-Orientation-Aware_Network_for_Real-Time_3D_Hand_Mesh_Reconstruction_CVPR_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_H2ONet_Hand-Occlusion-and-Orientation-Aware_Network_for_Real-Time_3D_Hand_Mesh_Reconstruction_CVPR_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":"h2onet-hand-occlusion-and-orientation-aware","repo_url":"https://github.com/hxwork/H2ONet_Pytorch","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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-hand-pose-estimation-on-dexycb","task":"3D Hand Pose Estimation","dataset":"DexYCB","model":"H2ONet","rank_in_archive_order":6,"of":11,"metrics":{"Average MPJPE (mm)":"14.0","MPVPE":"13.0","PA-MPVPE":"5.5","PA-VAUC":"89.1","Procrustes-Aligned MPJPE":"5.70","VAUC":"76.2"},"uses_additional_data":false},{"leaderboard":"/sota/3d-hand-pose-estimation-on-ho-3d","task":"3D Hand Pose Estimation","dataset":"HO-3D v2","model":"H2ONet","rank_in_archive_order":7,"of":24,"metrics":{"AUC_J":"0.829","AUC_V":"0.828","F@15mm":"0.966","F@5mm":"0.570","PA-MPJPE (mm)":"8.5","PA-MPVPE":"8.6"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}