{"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/arch-animatable-reconstruction-of-clothed","title":"ARCH: Animatable Reconstruction of Clothed Humans","arxiv_id":"2004.04572","date":"2020-04-08","proceeding":"CVPR 2020 6","authors":["Zeng Huang","Yuanlu Xu","Christoph Lassner","Hao Li","Tony Tung"],"abstract":"In this paper, we propose ARCH (Animatable Reconstruction of Clothed Humans), a novel end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image. Existing approaches to digitize 3D humans struggle to handle pose variations and recover details. Also, they do not produce models that are animation ready. In contrast, ARCH is a learned pose-aware model that produces detailed 3D rigged full-body human avatars from a single unconstrained RGB image. A Semantic Space and a Semantic Deformation Field are created using a parametric 3D body estimator. They allow the transformation of 2D/3D clothed humans into a canonical space, reducing ambiguities in geometry caused by pose variations and occlusions in training data. Detailed surface geometry and appearance are learned using an implicit function representation with spatial local features. Furthermore, we propose additional per-pixel supervision on the 3D reconstruction using opacity-aware differentiable rendering. Our experiments indicate that ARCH increases the fidelity of the reconstructed humans. We obtain more than 50% lower reconstruction errors for standard metrics compared to state-of-the-art methods on public datasets. We also show numerous qualitative examples of animated, high-quality reconstructed avatars unseen in the literature so far.","url_abs":"https://arxiv.org/abs/2004.04572v2","url_pdf":"https://arxiv.org/pdf/2004.04572v2.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":"arch-animatable-reconstruction-of-clothed","repo_url":"https://github.com/kuangzijian/drifu-for-animals","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"3d-object-reconstruction-from-a-single-image","task_name":"3D Object Reconstruction From A Single Image"},{"task_slug":"3d-reconstruction","task_name":"3D Reconstruction"}],"methods":[{"method_slug":"arch","method_name":"ARCH"}],"datasets_introduced":[],"methods_introduced":[{"slug":"arch","name":"ARCH","full_name":"Animatable Reconstruction of Clothed Humans"}],"results":[{"leaderboard":"/sota/3d-object-reconstruction-from-a-single-image-1","task":"3D Object Reconstruction From A Single Image","dataset":"BUFF","model":"ARCH","rank_in_archive_order":3,"of":5,"metrics":{"Chamfer (cm)":"0.87","Point-to-surface distance (cm)":"0.82","Surface normal consistency":"0.04"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2004.04572","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}