{"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/fitting-a-3d-morphable-model-to-edges-a","title":"Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences","arxiv_id":"1602.01125","date":"2016-02-02","proceeding":null,"authors":["Anil Bas","William A. P. Smith","Timo Bolkart","Stefanie Wuhrer"],"abstract":"We propose a fully automatic method for fitting a 3D morphable model to\nsingle face images in arbitrary pose and lighting. Our approach relies on\ngeometric features (edges and landmarks) and, inspired by the iterated closest\npoint algorithm, is based on computing hard correspondences between model\nvertices and edge pixels. We demonstrate that this is superior to previous work\nthat uses soft correspondences to form an edge-derived cost surface that is\nminimised by nonlinear optimisation.","url_abs":"http://arxiv.org/abs/1602.01125v2","url_pdf":"http://arxiv.org/pdf/1602.01125v2.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":"fitting-a-3d-morphable-model-to-edges-a","repo_url":"https://github.com/waps101/3DMM_edges","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1602.01125","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}