{"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/uv-idm-identity-conditioned-latent-diffusion","title":"UV-IDM: Identity-Conditioned Latent Diffusion Model for Face UV-Texture Generation","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Hong Li","Yutang Feng","Song Xue","Xuhui Liu","Bohan Zeng","Shanglin Li","Boyu Liu","Jianzhuang Liu","Shumin Han","Baochang Zhang"],"abstract":"    3D face reconstruction aims at generating high-fidelity 3D face shapes and textures from single-view or multi-view images. However current prevailing facial texture generation methods generally suffer from low-quality texture identity information loss and inadequate handling of occlusions. To solve these problems we introduce an Identity-Conditioned Latent Diffusion Model for face UV-texture generation (UV-IDM) to generate photo-realistic textures based on the Basel Face Model (BFM). UV-IDM leverages the powerful texture generation capacity of a latent diffusion model (LDM) to obtain detailed facial textures. To preserve the identity during the reconstruction procedure we design an identity-conditioned module that can utilize any in-the-wild image as a robust condition for the LDM to guide texture generation. UV-IDM can be easily adapted to different BFM-based methods as a high-fidelity texture generator. Furthermore in light of the limited accessibility of most existing UV-texture datasets we build a large-scale and publicly available UV-texture dataset based on BFM termed BFM-UV. Extensive experiments show that our UV-IDM can generate high-fidelity textures in 3D face reconstruction within seconds while maintaining image consistency bringing new state-of-the-art performance in facial texture generation.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Li_UV-IDM_Identity-Conditioned_Latent_Diffusion_Model_for_Face_UV-Texture_Generation_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Li_UV-IDM_Identity-Conditioned_Latent_Diffusion_Model_for_Face_UV-Texture_Generation_CVPR_2024_paper.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":"uv-idm-identity-conditioned-latent-diffusion","repo_url":"https://github.com/Luh1124/UV-IDM","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-face-reconstruction","task_name":"3D Face Reconstruction"},{"task_slug":"face-model","task_name":"Face Model"},{"task_slug":"face-reconstruction","task_name":"Face Reconstruction"},{"task_slug":"texture-synthesis","task_name":"Texture Synthesis"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"latent-diffusion-model","method_name":"Latent Diffusion Model"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}