TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation
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.
65fa696b0a9767ab235119db
Yufei Liu
claimed_verified
2024-03-25T09:44:18.229Z
645dd4a058f9ee3151493022
https://cdn-avatars.huggingface.co/v1/production/uploads/645dd4a058f9ee3151493022/2r0tgS90ww1vcQDLKbWCl.jpeg
Yufei Liu
user
ggxxii
65fa696b0a9767ab235119dc
Junwei Zhu
admin_assigned
2024-03-21T13:19:41.387Z
65aadb2b9aba49e1d03cfccc
/avatars/b7c48dc999bf2f94018315b546d50eeb.svg
Junwei Zhu
user
ktpt123
65fa696b0a9767ab235119dd
Junshu Tang
admin_assigned
2024-03-21T13:19:47.457Z
634e67f5d049354d7ee0caa4
/avatars/13f7a6f66060590e0394eb888148716c.svg
junshu tang
user
tangjs
65fa696b0a9767ab235119de
Shijie Zhang
65fa696b0a9767ab235119df
Jiangning Zhang
65fa696b0a9767ab235119e0
Weijian Cao
65fa696b0a9767ab235119e1
Chengjie Wang
admin_assigned
2024-03-21T13:20:24.486Z
65729087379284b904a6d81d
/avatars/0e7968b1d9221d523a54e3ac787e449d.svg
Chengjie Wang
user
chengjie-wang
65fa696b0a9767ab235119e2
Yunsheng Wu
65fa696b0a9767ab235119e3
Dongjin Huang
TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation
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.
TexDreamer is a zero-shot multimodal model that efficiently generates high-fidelity 3D human textures using semantic UV maps and a feature translator module.
zero-shot multimodal high-fidelity 3D human textures texture adaptation finetuning semantic UV structure feature translator module