TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation
Authors: name: Yufei Liu, name: Junwei Zhu, name: Junshu Tang, name: Shijie Zhang, name: Jiangning Zhang, name: Weijian Cao, name: Chengjie Wang, name: Yunsheng Wu, name: 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.