{"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/towards-high-fidelity-nonlinear-3d-face","title":"Towards High-fidelity Nonlinear 3D Face Morphable Model","arxiv_id":"1904.04933","date":"2019-04-09","proceeding":"CVPR 2019 6","authors":["Luan Tran","Feng Liu","Xiaoming Liu"],"abstract":"Embedding 3D morphable basis functions into deep neural networks opens great\npotential for models with better representation power. However, to faithfully\nlearn those models from an image collection, it requires strong regularization\nto overcome ambiguities involved in the learning process. This critically\nprevents us from learning high fidelity face models which are needed to\nrepresent face images in high level of details. To address this problem, this\npaper presents a novel approach to learn additional proxies as means to\nside-step strong regularizations, as well as, leverages to promote detailed\nshape/albedo. To ease the learning, we also propose to use a dual-pathway\nnetwork, a carefully-designed architecture that brings a balance between global\nand local-based models. By improving the nonlinear 3D morphable model in both\nlearning objective and network architecture, we present a model which is\nsuperior in capturing higher level of details than the linear or its precedent\nnonlinear counterparts. As a result, our model achieves state-of-the-art\nperformance on 3D face reconstruction by solely optimizing latent\nrepresentations.","url_abs":"http://arxiv.org/abs/1904.04933v1","url_pdf":"http://arxiv.org/pdf/1904.04933v1.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":[],"tasks":[{"task_slug":"3d-face-reconstruction","task_name":"3D Face Reconstruction"},{"task_slug":"face-reconstruction","task_name":"Face Reconstruction"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-face-reconstruction-on-realy","task":"3D Face Reconstruction","dataset":"REALY","model":"N-3DMM","rank_in_archive_order":24,"of":24,"metrics":{"@cheek":"1.918 (±0.801)","@forehead":"4.582 (±1.488)","@mouth":"2.375 (±0.599)","@nose":"2.936 (±0.810)","all":"2.953"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.04933","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}