title: RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion
    Models
summary: Text-to-texture generation has recently attracted increasing attention, but
  existing methods often suffer from the problems of view inconsistencies,
  apparent seams, and misalignment between textures and the underlying mesh. In
  this paper, we propose a robust text-to-texture method for generating
  consistent and seamless textures that are well aligned with the mesh. Our
  method leverages state-of-the-art 2D diffusion models, including SDXL and
  multiple ControlNets, to capture structural features and intricate details in
  the generated textures. The method also employs a symmetrical view synthesis
  strategy combined with regional prompts for enhancing view consistency.
  Additionally, it introduces novel texture blending and soft-inpainting
  techniques, which significantly reduce the seam regions. Extensive experiments
  demonstrate that our method outperforms existing state-of-the-art methods.
paper_authors._id: 67022b78ae1235c08ba4f564
paper_authors.name: Jangyeong Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-07T07:26:52.030Z
paper_authors._id: 634c1f9bb6628cbe2861dcc2
paper_authors.avatarUrl: /avatars/dd48dff0b639123c605b5c0ee10577d7.svg
paper_authors.fullname: Jangyeong.Kim
paper_authors.type: user
paper_authors.user: longshiine
paper_authors._id: 67022b78ae1235c08ba4f565
paper_authors.name: Donggoo Kang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-07T09:16:52.614Z
paper_authors._id: 62e772d0f97481d0a13f5766
paper_authors.avatarUrl: /avatars/69415574d0cbf0887a151519ee579669.svg
paper_authors.fullname: Donggoo Kang
paper_authors.type: user
paper_authors.user: DK9
paper_authors._id: 67022b78ae1235c08ba4f566
paper_authors.name: Junyoung Choi
paper_authors._id: 67022b78ae1235c08ba4f567
paper_authors.name: Jeonga Wi
paper_authors._id: 67022b78ae1235c08ba4f568
paper_authors.name: Junho Gwon
paper_authors._id: 67022b78ae1235c08ba4f569
paper_authors.name: Jiun Bae
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-07T09:17:12.639Z
paper_authors._id: 6304292d5c70c21d0eadf328
paper_authors.avatarUrl: /avatars/a5e312922ba5026f3f223de7c65f0c3c.svg
paper_authors.fullname: Jiun Bae
paper_authors.type: user
paper_authors.user: Jiun
paper_authors._id: 67022b78ae1235c08ba4f56a
paper_authors.name: Dumim Yoon
paper_authors._id: 67022b78ae1235c08ba4f56b
paper_authors.name: Junghyun Han
paper_title: RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion
    Models
paper_summary: Text-to-texture generation has recently attracted increasing attention, but
  existing methods often suffer from the problems of view inconsistencies,
  apparent seams, and misalignment between textures and the underlying mesh. In
  this paper, we propose a robust text-to-texture method for generating
  consistent and seamless textures that are well aligned with the mesh. Our
  method leverages state-of-the-art 2D diffusion models, including SDXL and
  multiple ControlNets, to capture structural features and intricate details in
  the generated textures. The method also employs a symmetrical view synthesis
  strategy combined with regional prompts for enhancing view consistency.
  Additionally, it introduces novel texture blending and soft-inpainting
  techniques, which significantly reduce the seam regions. Extensive experiments
  demonstrate that our method outperforms existing state-of-the-art methods.
paper_ai_keywords: diffusion models SDXL ControlNets symmetrical view synthesis regional prompts texture blending soft-inpainting