{"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/deephuman-3d-human-reconstruction-from-a","title":"DeepHuman: 3D Human Reconstruction from a Single Image","arxiv_id":"1903.06473","date":"2019-03-15","proceeding":"ICCV 2019 10","authors":["Zerong Zheng","Tao Yu","Yixuan Wei","Qionghai Dai","Yebin Liu"],"abstract":"We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D\nhuman reconstruction from a single RGB image. To reduce the ambiguities\nassociated with the surface geometry reconstruction, even for the\nreconstruction of invisible areas, we propose and leverage a dense semantic\nrepresentation generated from SMPL model as an additional input. One key\nfeature of our network is that it fuses different scales of image features into\nthe 3D space through volumetric feature transformation, which helps to recover\naccurate surface geometry. The visible surface details are further refined\nthrough a normal refinement network, which can be concatenated with the volume\ngeneration network using our proposed volumetric normal projection layer. We\nalso contribute THuman, a 3D real-world human model dataset containing about\n7000 models. The network is trained using training data generated from the\ndataset. Overall, due to the specific design of our network and the diversity\nin our dataset, our method enables 3D human model estimation given only a\nsingle image and outperforms state-of-the-art approaches.","url_abs":"http://arxiv.org/abs/1903.06473v2","url_pdf":"http://arxiv.org/pdf/1903.06473v2.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":"deephuman-3d-human-reconstruction-from-a","repo_url":"https://github.com/ZhengZerong/DeepHuman","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"3d-human-reconstruction","task_name":"3D Human Reconstruction"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.06473","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}