title: TUVF: Learning Generalizable Texture UV Radiance Fields
summary: Textures are a vital aspect of creating visually appealing and realistic 3D
  models. In this paper, we study the problem of generating high-fidelity texture
  given shapes of 3D assets, which has been relatively less explored compared
  with generic 3D shape modeling. Our goal is to facilitate a controllable
  texture generation process, such that one texture code can correspond to a
  particular appearance style independent of any input shapes from a category. We
  introduce Texture UV Radiance Fields (TUVF) that generate textures in a
  learnable UV sphere space rather than directly on the 3D shape. This allows the
  texture to be disentangled from the underlying shape and transferable to other
  shapes that share the same UV space, i.e., from the same category. We integrate
  the UV sphere space with the radiance field, which provides a more efficient
  and accurate representation of textures than traditional texture maps. We
  perform our experiments on real-world object datasets where we achieve not only
  realistic synthesis but also substantial improvements over state-of-the-arts on
  texture controlling and editing. Project Page: https://www.anjiecheng.me/TUVF
paper_authors._id: 645484b388c97c8796dd268b
paper_authors.name: An-Chieh Cheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-25T10:31:18.127Z
paper_authors._id: 63d07b75f2341424c80a5de8
paper_authors.avatarUrl: /avatars/7371aa17e785bdcb3c85fa122b5b5d3e.svg
paper_authors.fullname: An-Chieh Cheng
paper_authors.type: user
paper_authors.user: a8cheng
paper_authors._id: 645484b388c97c8796dd268c
paper_authors.name: Xueting Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T13:20:43.876Z
paper_authors._id: 6263685063f73be3d2f97604
paper_authors.avatarUrl: /avatars/9c5d2f81cfd84bc835e4feb11b5e60b3.svg
paper_authors.fullname: Xueting Li
paper_authors.type: user
paper_authors.user: sunshineatnoon
paper_authors._id: 645484b388c97c8796dd268d
paper_authors.name: Sifei Liu
paper_authors._id: 645484b388c97c8796dd268e
paper_authors.name: Xiaolong Wang
paper_title: TUVF: Learning Generalizable Texture UV Radiance Fields
paper_summary: Textures are a vital aspect of creating visually appealing and realistic 3D
  models. In this paper, we study the problem of generating high-fidelity texture
  given shapes of 3D assets, which has been relatively less explored compared
  with generic 3D shape modeling. Our goal is to facilitate a controllable
  texture generation process, such that one texture code can correspond to a
  particular appearance style independent of any input shapes from a category. We
  introduce Texture UV Radiance Fields (TUVF) that generate textures in a
  learnable UV sphere space rather than directly on the 3D shape. This allows the
  texture to be disentangled from the underlying shape and transferable to other
  shapes that share the same UV space, i.e., from the same category. We integrate
  the UV sphere space with the radiance field, which provides a more efficient
  and accurate representation of textures than traditional texture maps. We
  perform our experiments on real-world object datasets where we achieve not only
  realistic synthesis but also substantial improvements over state-of-the-arts on
  texture controlling and editing. Project Page: https://www.anjiecheng.me/TUVF
paper_ai_summary: Texture UV Radiance Fields (TUVF) enable controllable and transferable texture generation for 3D models by learning textures in a UV sphere space.
paper_ai_keywords: Texture UV Radiance Fields (TUVF) UV sphere space radiance field texture maps texture controlling texture editing