title: HiFA: High-fidelity Text-to-3D with Advanced Diffusion Guidance
summary: Automatic text-to-3D synthesis has achieved remarkable advancements through
  the optimization of 3D models. Existing methods commonly rely on pre-trained
  text-to-image generative models, such as diffusion models, providing scores for
  2D renderings of Neural Radiance Fields (NeRFs) and being utilized for
  optimizing NeRFs. However, these methods often encounter artifacts and
  inconsistencies across multiple views due to their limited understanding of 3D
  geometry. To address these limitations, we propose a reformulation of the
  optimization loss using the diffusion prior. Furthermore, we introduce a novel
  training approach that unlocks the potential of the diffusion prior. To improve
  3D geometry representation, we apply auxiliary depth supervision for
  NeRF-rendered images and regularize the density field of NeRFs. Extensive
  experiments demonstrate the superiority of our method over prior works,
  resulting in advanced photo-realism and improved multi-view consistency.
paper_authors._id: 6476bbe183d4fdaedddf9100
paper_authors.name: Joseph Zhu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-01T08:12:11.389Z
paper_authors._id: 60be3d8f2d0691c26ddabf22
paper_authors.avatarUrl: /avatars/9eddff30bb54277a6877cecf517ce8b5.svg
paper_authors.fullname: Joseph Zhu
paper_authors.type: user
paper_authors.user: JunzheJosephZhu
paper_authors._id: 6476bbe183d4fdaedddf9101
paper_authors.name: Peiye Zhuang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-01T08:13:27.389Z
paper_authors._id: 63e473cf9db5da2dc1f4e9ff
paper_authors.avatarUrl: /avatars/6973b4c959ef8bb72c40c611d384a78a.svg
paper_authors.fullname: peiye zhuang
paper_authors.type: user
paper_authors.user: Kelest
paper_title: HiFA: High-fidelity Text-to-3D with Advanced Diffusion Guidance
paper_summary: Automatic text-to-3D synthesis has achieved remarkable advancements through
  the optimization of 3D models. Existing methods commonly rely on pre-trained
  text-to-image generative models, such as diffusion models, providing scores for
  2D renderings of Neural Radiance Fields (NeRFs) and being utilized for
  optimizing NeRFs. However, these methods often encounter artifacts and
  inconsistencies across multiple views due to their limited understanding of 3D
  geometry. To address these limitations, we propose a reformulation of the
  optimization loss using the diffusion prior. Furthermore, we introduce a novel
  training approach that unlocks the potential of the diffusion prior. To improve
  3D geometry representation, we apply auxiliary depth supervision for
  NeRF-rendered images and regularize the density field of NeRFs. Extensive
  experiments demonstrate the superiority of our method over prior works,
  resulting in advanced photo-realism and improved multi-view consistency.
paper_ai_keywords: diffusion models Neural Radiance Fields (NeRFs) diffusion prior optimization loss auxiliary depth supervision density field