TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation
Yufei Liu, Junwei Zhu, Junshu Tang, Shijie Zhang, Jiangning Zhang, Weijian Cao, Chengjie Wang, Yunsheng Wu
Abstract
"Texturing 3D humans with semantic UV maps remains a challenge due to the difficulty of acquiring reasonably unfolded UV. Despite recent text-to-3D advancements in supervising multi-view renderings using large text-to-image (T2I) models, issues persist with generation speed, text consistency, and texture quality, resulting in data scarcity among existing datasets. We present TexDreamer, the first zero-shot multimodal high-fidelity 3D human texture generation model. Utilizing an efficient texture adaptation finetuning strategy, we adapt large T2I model to a semantic UV structure while preserving its original generalization capability. Leveraging a novel feature translator module, the trained model is capable of generating high-fidelity 3D human textures from either text or image within seconds. Furthermore, we introduce ArTicuLated humAn textureS (ATLAS), the largest high-resolution (1, 024×1, 024) 3D human texture dataset which contains 50k high-fidelity textures with text descriptions."
BibTeX
@inproceedings{eccv2024_texdreamertoward,
title = {TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation},
author = {Yufei Liu and Junwei Zhu and Junshu Tang and Shijie Zhang and Jiangning Zhang and Weijian Cao and Chengjie Wang and Yunsheng Wu and Dongjin Huang*},
booktitle = {ECCV 2024},
year = {2024}
}