UV-IDM: Identity-Conditioned Latent Diffusion Model for Face UV-Texture Generation

Hong Li, Yutang Feng, Song Xue, Xuhui Liu, Bohan Zeng, Shanglin Li, Boyu Liu, Jianzhuang Liu, Shumin Han, Baochang Zhang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 10585-10595

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.

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[bibtex]
@InProceedings{Li_2024_CVPR, author = {Li, Hong and Feng, Yutang and Xue, Song and Liu, Xuhui and Zeng, Bohan and Li, Shanglin and Liu, Boyu and Liu, Jianzhuang and Han, Shumin and Zhang, Baochang}, title = {UV-IDM: Identity-Conditioned Latent Diffusion Model for Face UV-Texture Generation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {10585-10595} }