title: TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation
summary: 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.
paper_authors._id: 65fa696b0a9767ab235119db
paper_authors.name: Yufei Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-25T09:44:18.229Z
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paper_authors.fullname: Yufei Liu
paper_authors.type: user
paper_authors.user: ggxxii
paper_authors._id: 65fa696b0a9767ab235119dc
paper_authors.name: Junwei Zhu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T13:19:41.387Z
paper_authors._id: 65aadb2b9aba49e1d03cfccc
paper_authors.avatarUrl: /avatars/b7c48dc999bf2f94018315b546d50eeb.svg
paper_authors.fullname: Junwei Zhu
paper_authors.type: user
paper_authors.user: ktpt123
paper_authors._id: 65fa696b0a9767ab235119dd
paper_authors.name: Junshu Tang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T13:19:47.457Z
paper_authors._id: 634e67f5d049354d7ee0caa4
paper_authors.avatarUrl: /avatars/13f7a6f66060590e0394eb888148716c.svg
paper_authors.fullname: junshu tang
paper_authors.type: user
paper_authors.user: tangjs
paper_authors._id: 65fa696b0a9767ab235119de
paper_authors.name: Shijie Zhang
paper_authors._id: 65fa696b0a9767ab235119df
paper_authors.name: Jiangning Zhang
paper_authors._id: 65fa696b0a9767ab235119e0
paper_authors.name: Weijian Cao
paper_authors._id: 65fa696b0a9767ab235119e1
paper_authors.name: Chengjie Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T13:20:24.486Z
paper_authors._id: 65729087379284b904a6d81d
paper_authors.avatarUrl: /avatars/0e7968b1d9221d523a54e3ac787e449d.svg
paper_authors.fullname: Chengjie Wang
paper_authors.type: user
paper_authors.user: chengjie-wang
paper_authors._id: 65fa696b0a9767ab235119e2
paper_authors.name: Yunsheng Wu
paper_authors._id: 65fa696b0a9767ab235119e3
paper_authors.name: Dongjin Huang
paper_title: TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation
paper_summary: 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.
paper_ai_summary: TexDreamer is a zero-shot multimodal model that efficiently generates high-fidelity 3D human textures using semantic UV maps and a feature translator module.
paper_ai_keywords: zero-shot multimodal high-fidelity 3D human textures texture adaptation finetuning semantic UV structure feature translator module