DreamPolish: Domain Score Distillation With Progressive Geometry
  Generation
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.
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Yean Cheng
claimed_verified
2025-01-07T08:43:07.500Z
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Yean Cheng
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LiquidAmmonia
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Ziqi Cai
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2024-11-07T00:23:42.529Z
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Ziqi  Cai
user
GhostCai
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Ming Ding
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Wendi Zheng
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Shiyu Huang
claimed_verified
2025-07-02T09:13:52.401Z
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Shiyu Huang
user
ShiyuHuang
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Yuxiao Dong
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Jie Tang
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Boxin Shi
DreamPolish: Domain Score Distillation With Progressive Geometry
  Generation
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.
DreamPolish generates high-quality 3D assets with refined geometry and photorealistic textures using multiple neural representations, normal estimation, and a novel score distillation objective.
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