{"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-3d-morphable-models-using-local","title":"Fitting 3D Morphable Models using Local Features","arxiv_id":"1503.02330","date":"2015-03-08","proceeding":null,"authors":["Patrik Huber","Zhen-Hua Feng","William Christmas","Josef Kittler","Matthias Rätsch"],"abstract":"In this paper, we propose a novel fitting method that uses local image\nfeatures to fit a 3D Morphable Model to 2D images. To overcome the obstacle of\noptimising a cost function that contains a non-differentiable feature\nextraction operator, we use a learning-based cascaded regression method that\nlearns the gradient direction from data. The method allows to simultaneously\nsolve for shape and pose parameters. Our method is thoroughly evaluated on\nMorphable Model generated data and first results on real data are presented.\nCompared to traditional fitting methods, which use simple raw features like\npixel colour or edge maps, local features have been shown to be much more\nrobust against variations in imaging conditions. Our approach is unique in that\nwe are the first to use local features to fit a Morphable Model.\n  Because of the speed of our method, it is applicable for realtime\napplications. Our cascaded regression framework is available as an open source\nlibrary (https://github.com/patrikhuber).","url_abs":"http://arxiv.org/abs/1503.02330v1","url_pdf":"http://arxiv.org/pdf/1503.02330v1.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-3d-morphable-models-using-local","repo_url":"https://github.com/patrikhuber/superviseddescent","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}