title: TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and
    Resampling
summary: Given a 3D mesh, we aim to synthesize 3D textures that correspond to
  arbitrary textual descriptions. Current methods for generating and assembling
  textures from sampled views often result in prominent seams or excessive
  smoothing. To tackle these issues, we present TexGen, a novel multi-view
  sampling and resampling framework for texture generation leveraging a
  pre-trained text-to-image diffusion model. For view consistent sampling, first
  of all we maintain a texture map in RGB space that is parameterized by the
  denoising step and updated after each sampling step of the diffusion model to
  progressively reduce the view discrepancy. An attention-guided multi-view
  sampling strategy is exploited to broadcast the appearance information across
  views. To preserve texture details, we develop a noise resampling technique
  that aids in the estimation of noise, generating inputs for subsequent
  denoising steps, as directed by the text prompt and current texture map.
  Through an extensive amount of qualitative and quantitative evaluations, we
  demonstrate that our proposed method produces significantly better texture
  quality for diverse 3D objects with a high degree of view consistency and rich
  appearance details, outperforming current state-of-the-art methods.
  Furthermore, our proposed texture generation technique can also be applied to
  texture editing while preserving the original identity. More experimental
  results are available at https://dong-huo.github.io/TexGen/
paper_authors._id: 66b03b6f06c013ef866bca28
paper_authors.name: Dong Huo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-05T14:33:34.474Z
paper_authors._id: 64ca6be076200ec80fce102f
paper_authors.avatarUrl: /avatars/e587d5bc22edf90aae4e6a681ffc56a8.svg
paper_authors.fullname: Philip Huo
paper_authors.type: user
paper_authors.user: Philip-Huo
paper_authors._id: 66b03b6f06c013ef866bca29
paper_authors.name: Zixin Guo
paper_authors._id: 66b03b6f06c013ef866bca2a
paper_authors.name: Xinxin Zuo
paper_authors._id: 66b03b6f06c013ef866bca2b
paper_authors.name: Zhihao Shi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-05T09:20:13.860Z
paper_authors._id: 64c243175b329de1b1e6a048
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64c243175b329de1b1e6a048/puCPybuNK1L62V5nRLttQ.jpeg
paper_authors.fullname: Zhihao Shi
paper_authors.type: user
paper_authors.user: somniis
paper_authors._id: 66b03b6f06c013ef866bca2c
paper_authors.name: Juwei Lu
paper_authors._id: 66b03b6f06c013ef866bca2d
paper_authors.name: Peng Dai
paper_authors._id: 66b03b6f06c013ef866bca2e
paper_authors.name: Songcen Xu
paper_authors._id: 66b03b6f06c013ef866bca2f
paper_authors.name: Li Cheng
paper_authors._id: 66b03b6f06c013ef866bca30
paper_authors.name: Yee-Hong Yang
paper_title: TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and
    Resampling
paper_summary: Given a 3D mesh, we aim to synthesize 3D textures that correspond to
  arbitrary textual descriptions. Current methods for generating and assembling
  textures from sampled views often result in prominent seams or excessive
  smoothing. To tackle these issues, we present TexGen, a novel multi-view
  sampling and resampling framework for texture generation leveraging a
  pre-trained text-to-image diffusion model. For view consistent sampling, first
  of all we maintain a texture map in RGB space that is parameterized by the
  denoising step and updated after each sampling step of the diffusion model to
  progressively reduce the view discrepancy. An attention-guided multi-view
  sampling strategy is exploited to broadcast the appearance information across
  views. To preserve texture details, we develop a noise resampling technique
  that aids in the estimation of noise, generating inputs for subsequent
  denoising steps, as directed by the text prompt and current texture map.
  Through an extensive amount of qualitative and quantitative evaluations, we
  demonstrate that our proposed method produces significantly better texture
  quality for diverse 3D objects with a high degree of view consistency and rich
  appearance details, outperforming current state-of-the-art methods.
  Furthermore, our proposed texture generation technique can also be applied to
  texture editing while preserving the original identity. More experimental
  results are available at https://dong-huo.github.io/TexGen/
paper_ai_summary: TexGen, a novel framework using a text-to-image diffusion model, generates high-quality, viewpoint-consistent 3D textures from textual descriptions, improving upon current methods.
paper_ai_keywords: multi-view sampling resampling framework text-to-image diffusion model denoising step attention-guided sampling noise resampling view consistency texture quality appearance details texture editing