{"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/deformable-mesh-transformer-for-3d-human-mesh","title":"Deformable Mesh Transformer for 3D Human Mesh Recovery","arxiv_id":null,"date":"2023-01-01","proceeding":"CVPR 2023 1","authors":["Yusuke Yoshiyasu"],"abstract":"    We present Deformable mesh transFormer (DeFormer), a novel vertex-based approach to monocular 3D human mesh recovery. DeFormer iteratively fits a body mesh model to an input image via a mesh alignment feedback loop formed within a transformer decoder that is equipped with efficient body mesh driven attention modules: 1) body sparse self-attention and 2) deformable mesh cross attention. As a result, DeFormer can effectively exploit high-resolution image feature maps and a dense mesh model which were computationally expensive to deal with in previous approaches using the standard transformer attention. Experimental results show that DeFormer achieves state-of-the-art performances on the Human3.6M and 3DPW benchmarks. Ablation study is also conducted to show the effectiveness of the DeFormer model designs for leveraging multi-scale feature maps. Code is available at https://github.com/yusukey03012/DeFormer.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2023/html/Yoshiyasu_Deformable_Mesh_Transformer_for_3D_Human_Mesh_Recovery_CVPR_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2023/papers/Yoshiyasu_Deformable_Mesh_Transformer_for_3D_Human_Mesh_Recovery_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":"deformable-mesh-transformer-for-3d-human-mesh","repo_url":"https://github.com/yusukey03012/deformer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"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":"decoder","task_name":"Decoder"},{"task_slug":"human-mesh-recovery","task_name":"Human Mesh Recovery"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-hand-pose-estimation-on-freihand","task":"3D Hand Pose Estimation","dataset":"FreiHAND","model":"Deformer","rank_in_archive_order":13,"of":33,"metrics":{"PA-F@15mm":"0.984","PA-F@5mm":"0.743","PA-MPJPE":"6.2","PA-MPVPE":"6.4"},"uses_additional_data":false},{"leaderboard":"/sota/3d-human-pose-estimation-on-3dpw","task":"3D Human Pose Estimation","dataset":"3DPW","model":"DeFormer","rank_in_archive_order":25,"of":119,"metrics":{"MPJPE":"72.9","MPVPE":"82.6","PA-MPJPE":"44.3"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}