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 Dongjin Huang
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 (1024 X 1024)
3D human texture dataset which contains 50k high-fidelity textures with text
descriptions.