{"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/bcnet-learning-body-and-cloth-shape-from-a","title":"BCNet: Learning Body and Cloth Shape from A Single Image","arxiv_id":"2004.00214","date":"2020-04-01","proceeding":"ECCV 2020 8","authors":["Boyi Jiang","Juyong Zhang","Yang Hong","Jinhao Luo","Ligang Liu","Hujun Bao"],"abstract":"In this paper, we consider the problem to automatically reconstruct garment and body shapes from a single near-front view RGB image. To this end, we propose a layered garment representation on top of SMPL and novelly make the skinning weight of garment independent of the body mesh, which significantly improves the expression ability of our garment model. Compared with existing methods, our method can support more garment categories and recover more accurate geometry. To train our model, we construct two large scale datasets with ground truth body and garment geometries as well as paired color images. Compared with single mesh or non-parametric representation, our method can achieve more flexible control with separate meshes, makes applications like re-pose, garment transfer, and garment texture mapping possible. Code and some data is available at https://github.com/jby1993/BCNet.","url_abs":"https://arxiv.org/abs/2004.00214v2","url_pdf":"https://arxiv.org/pdf/2004.00214v2.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":"bcnet-learning-body-and-cloth-shape-from-a","repo_url":"https://github.com/jby1993/BCNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"garment-reconstruction","task_name":"Garment Reconstruction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/garment-reconstruction-on-4d-dress","task":"Garment Reconstruction","dataset":"4D-DRESS","model":"BCNet_Upper","rank_in_archive_order":1,"of":9,"metrics":{"Chamfer (cm)":"2.079","IOU":"0.700"},"uses_additional_data":true},{"leaderboard":"/sota/garment-reconstruction-on-4d-dress","task":"Garment Reconstruction","dataset":"4D-DRESS","model":"BCNet_Lower","rank_in_archive_order":4,"of":9,"metrics":{"Chamfer (cm)":"2.533","IOU":"0.675"},"uses_additional_data":true},{"leaderboard":"/sota/garment-reconstruction-on-4d-dress","task":"Garment Reconstruction","dataset":"4D-DRESS","model":"BCNet_Outer","rank_in_archive_order":8,"of":9,"metrics":{"Chamfer (cm)":"3.600","IOU":"0.639"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/2004.00214","atlas_url":"https://app.syntology.ai/?focus=2004.00214","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.00214"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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