{"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/from-2d-to-3d-geodesic-based-garment-matching","title":"From 2D to 3D Geodesic-based Garment Matching","arxiv_id":"1809.08064","date":"2018-09-21","proceeding":null,"authors":["Meysam Madadi","Egils Avots","Sergio Escalera","Jordi Gonzalez","Xavier Baro","Gholamreza Anbarjafari"],"abstract":"A new approach for 2D to 3D garment retexturing is proposed based on Gaussian\nmixture models and thin plate splines (TPS). An automatically segmented garment\nof an individual is matched to a new source garment and rendered, resulting in\naugmented images in which the target garment has been retextured by using the\ntexture of the source garment. We divide the problem into garment boundary\nmatching based on Gaussian mixture models and then interpolate inner points\nusing surface topology extracted through geodesic paths, which leads to a more\nrealistic result than standard approaches. We evaluated and compared our system\nquantitatively by mean square error (MSE) and qualitatively using the mean\nopinion score (MOS), showing the benefits of the proposed methodology on our\ngathered dataset.","url_abs":"http://arxiv.org/abs/1809.08064v1","url_pdf":"http://arxiv.org/pdf/1809.08064v1.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":"from-2d-to-3d-geodesic-based-garment-matching","repo_url":"https://github.com/bing-jian/gmmreg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}