{"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/a-probabilistic-attention-model-with","title":"A Probabilistic Attention Model with Occlusion-aware Texture Regression for 3D Hand Reconstruction from a Single RGB Image","arxiv_id":"2304.14299","date":"2023-04-27","proceeding":"CVPR 2023 1","authors":["Zheheng Jiang","Hossein Rahmani","Sue Black","Bryan M. Williams"],"abstract":"Recently, deep learning based approaches have shown promising results in 3D hand reconstruction from a single RGB image. These approaches can be roughly divided into model-based approaches, which are heavily dependent on the model's parameter space, and model-free approaches, which require large numbers of 3D ground truths to reduce depth ambiguity and struggle in weakly-supervised scenarios. To overcome these issues, we propose a novel probabilistic model to achieve the robustness of model-based approaches and reduced dependence on the model's parameter space of model-free approaches. The proposed probabilistic model incorporates a model-based network as a prior-net to estimate the prior probability distribution of joints and vertices. An Attention-based Mesh Vertices Uncertainty Regression (AMVUR) model is proposed to capture dependencies among vertices and the correlation between joints and mesh vertices to improve their feature representation. We further propose a learning based occlusion-aware Hand Texture Regression model to achieve high-fidelity texture reconstruction. We demonstrate the flexibility of the proposed probabilistic model to be trained in both supervised and weakly-supervised scenarios. The experimental results demonstrate our probabilistic model's state-of-the-art accuracy in 3D hand and texture reconstruction from a single image in both training schemes, including in the presence of severe occlusions.","url_abs":"https://arxiv.org/abs/2304.14299v1","url_pdf":"https://arxiv.org/pdf/2304.14299v1.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":"a-probabilistic-attention-model-with","repo_url":"https://github.com/zhehengjianglancaster/amvur","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"3d-hand-pose-estimation","task_name":"3D Hand Pose Estimation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-hand-pose-estimation-on-freihand","task":"3D Hand Pose Estimation","dataset":"FreiHAND","model":"AMVUR","rank_in_archive_order":12,"of":33,"metrics":{"PA-F@15mm":"0.987","PA-F@5mm":"0.767","PA-MPJPE":"6.2","PA-MPVPE":"6.1"},"uses_additional_data":false},{"leaderboard":"/sota/3d-hand-pose-estimation-on-ho-3d","task":"3D Hand Pose Estimation","dataset":"HO-3D v2","model":"AMVUR","rank_in_archive_order":6,"of":24,"metrics":{"AUC_J":"0.835","AUC_V":"0.836","F@15mm":"0.965","F@5mm":"0.608","PA-MPJPE (mm)":"8.3","PA-MPVPE":"8.2"},"uses_additional_data":false},{"leaderboard":"/sota/3d-hand-pose-estimation-on-ho-3d-v3","task":"3D Hand Pose Estimation","dataset":"HO-3D v3","model":"AMVUR","rank_in_archive_order":3,"of":8,"metrics":{"AUC_J":"0.826","AUC_V":"0.834","F@15mm":"0.964","F@5mm":"0.593","PA-MPJPE":"8.7","PA-MPVPE":"8.3"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2304.14299","atlas_url":"https://app.syntology.ai/?focus=2304.14299","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}