DreamPolish: Domain Score Distillation With Progressive Geometry Generation
Yean Cheng, Ziqi Cai, Ming Ding, Wendi Zheng, Shiyu Huang, Yuxiao, Dong, Jie Tang, Boxin Shi

TL;DR
DreamPolish is a novel text-to-3D generation framework that improves geometry stability and texture quality by incorporating multiple neural representations, a surface polishing stage, and a domain score distillation method inspired by classifier-free guidance.
Contribution
It introduces a new geometry refinement technique with a normal estimator and a surface polishing stage, along with a domain score distillation approach for enhanced texture generation.
Findings
Produces 3D assets with refined geometry and photorealistic textures.
Outperforms existing state-of-the-art methods in quality.
Effective geometry and texture refinement with minimal training steps.
Abstract
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…
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Taxonomy
TopicsHuman Motion and Animation · Video Analysis and Summarization · Handwritten Text Recognition Techniques
MethodsDiffusion
