title: DreamPolish: Domain Score Distillation With Progressive Geometry
    Generation
summary: We introduce DreamPolish, a text-to-3D generation model that excels in
  producing refined geometry and high-quality textures. In the geometry
  construction phase, our approach leverages multiple neural representations to
  enhance the stability of the synthesis process. Instead of relying solely on a
  view-conditioned diffusion prior in the novel sampled views, which often leads
  to undesired artifacts in the geometric surface, we incorporate an additional
  normal estimator to polish the geometry details, conditioned on viewpoints with
  varying field-of-views. We propose to add a surface polishing stage with only a
  few training steps, which can effectively refine the artifacts attributed to
  limited guidance from previous stages and produce 3D objects with more
  desirable geometry. The key topic of texture generation using pretrained
  text-to-image models is to find a suitable domain in the vast latent
  distribution of these models that contains photorealistic and consistent
  renderings. In the texture generation phase, we introduce a novel score
  distillation objective, namely domain score distillation (DSD), to guide neural
  representations toward such a domain. We draw inspiration from the
  classifier-free guidance (CFG) in textconditioned image generation tasks and
  show that CFG and variational distribution guidance represent distinct aspects
  in gradient guidance and are both imperative domains for the enhancement of
  texture quality. Extensive experiments show our proposed model can produce 3D
  assets with polished surfaces and photorealistic textures, outperforming
  existing state-of-the-art methods.
paper_authors._id: 672afacd7b58518d3534070f
paper_authors.name: Yean Cheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-07T08:43:07.500Z
paper_authors._id: 65acc5afe2a2c8635614de43
paper_authors.avatarUrl: /avatars/c5fce792792cc0b52ed7475d72460c58.svg
paper_authors.fullname: Yean Cheng
paper_authors.type: user
paper_authors.user: LiquidAmmonia
paper_authors._id: 672afacd7b58518d35340710
paper_authors.name: Ziqi Cai
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-11-07T00:23:42.529Z
paper_authors._id: 659fa7002c4538d113c7296a
paper_authors.avatarUrl: /avatars/eff46ce65ce83e09b8223bad335cd0fc.svg
paper_authors.fullname: Ziqi  Cai
paper_authors.type: user
paper_authors.user: GhostCai
paper_authors._id: 672afacd7b58518d35340711
paper_authors.name: Ming Ding
paper_authors._id: 672afacd7b58518d35340712
paper_authors.name: Wendi Zheng
paper_authors._id: 672afacd7b58518d35340713
paper_authors.name: Shiyu Huang
paper_authors._id: 672afacd7b58518d35340714
paper_authors.name: Yuxiao Dong
paper_authors._id: 672afacd7b58518d35340715
paper_authors.name: Jie Tang
paper_authors._id: 672afacd7b58518d35340716
paper_authors.name: Boxin Shi
paper_title: DreamPolish: Domain Score Distillation With Progressive Geometry
    Generation
paper_summary: We introduce DreamPolish, a text-to-3D generation model that excels in
  producing refined geometry and high-quality textures. In the geometry
  construction phase, our approach leverages multiple neural representations to
  enhance the stability of the synthesis process. Instead of relying solely on a
  view-conditioned diffusion prior in the novel sampled views, which often leads
  to undesired artifacts in the geometric surface, we incorporate an additional
  normal estimator to polish the geometry details, conditioned on viewpoints with
  varying field-of-views. We propose to add a surface polishing stage with only a
  few training steps, which can effectively refine the artifacts attributed to
  limited guidance from previous stages and produce 3D objects with more
  desirable geometry. The key topic of texture generation using pretrained
  text-to-image models is to find a suitable domain in the vast latent
  distribution of these models that contains photorealistic and consistent
  renderings. In the texture generation phase, we introduce a novel score
  distillation objective, namely domain score distillation (DSD), to guide neural
  representations toward such a domain. We draw inspiration from the
  classifier-free guidance (CFG) in textconditioned image generation tasks and
  show that CFG and variational distribution guidance represent distinct aspects
  in gradient guidance and are both imperative domains for the enhancement of
  texture quality. Extensive experiments show our proposed model can produce 3D
  assets with polished surfaces and photorealistic textures, outperforming
  existing state-of-the-art methods.
paper_ai_summary: DreamPolish generates high-quality 3D assets with refined geometry and photorealistic textures using multiple neural representations, normal estimation, and a novel score distillation objective.
paper_ai_keywords: text-to-3D generation neural representations diffusion prior normal estimator surface polishing texture generation pretrained text-to-image models latent distribution domain score distillation classifier-free guidance variational distribution guidance gradient guidance