{"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/dense-face-alignment","title":"Dense Face Alignment","arxiv_id":"1709.01442","date":"2017-09-05","proceeding":null,"authors":["Yaojie Liu","Amin Jourabloo","William Ren","Xiaoming Liu"],"abstract":"Face alignment is a classic problem in the computer vision field. Previous\nworks mostly focus on sparse alignment with a limited number of facial landmark\npoints, i.e., facial landmark detection. In this paper, for the first time, we\naim at providing a very dense 3D alignment for large-pose face images. To\nachieve this, we train a CNN to estimate the 3D face shape, which not only\naligns limited facial landmarks but also fits face contours and SIFT feature\npoints. Moreover, we also address the bottleneck of training CNN with multiple\ndatasets, due to different landmark markups on different datasets, such as 5,\n34, 68. Experimental results show our method not only provides high-quality,\ndense 3D face fitting but also outperforms the state-of-the-art facial landmark\ndetection methods on the challenging datasets. Our model can run at real time\nduring testing.","url_abs":"http://arxiv.org/abs/1709.01442v1","url_pdf":"http://arxiv.org/pdf/1709.01442v1.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":"dense-face-alignment","repo_url":"https://github.com/yaojieliu/ICCVW2017-DenseFaceAlignment","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"3d-face-reconstruction","task_name":"3D Face Reconstruction"},{"task_slug":"face-alignment","task_name":"Face Alignment"},{"task_slug":"facial-landmark-detection","task_name":"Facial Landmark Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-face-reconstruction-on-aflw2000-3d","task":"3D Face Reconstruction","dataset":"AFLW2000-3D","model":"DeFA","rank_in_archive_order":7,"of":8,"metrics":{"Mean NME ":"5.6454%"},"uses_additional_data":false},{"leaderboard":"/sota/face-alignment-on-aflw-lfpa","task":"Face Alignment","dataset":"AFLW-LFPA","model":"DeFA","rank_in_archive_order":2,"of":3,"metrics":{"Mean NME ":"3.86%"},"uses_additional_data":false},{"leaderboard":"/sota/face-alignment-on-aflw2000-3d","task":"Face Alignment","dataset":"AFLW2000-3D","model":"DeFA","rank_in_archive_order":12,"of":14,"metrics":{"Balanced NME (2D Sparse Alignment)":"4.50%","Mean NME(3D Dense Alignment)":"6.04%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.01442","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}