Summary of Dreampolish: Domain Score Distillation with Progressive Geometry Generation, by Yean Cheng et al.
DreamPolish: Domain Score Distillation With Progressive Geometry Generation
by Yean Cheng, Ziqi Cai, Ming Ding, Wendi Zheng, Shiyu Huang, Yuxiao Dong, Jie Tang, Boxin Shi
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We present DreamPolish, a text-to-3D generation model that excels in producing refined geometry and high-quality textures. The model leverages multiple neural representations to enhance the stability of the synthesis process, incorporating an additional normal estimator to polish geometry details conditioned on varying viewpoints. A surface polishing stage is proposed with only a few training steps, refining artifacts and producing 3D objects with desirable geometry. In texture generation, we introduce domain score distillation (DSD) to guide neural representations toward photorealistic and consistent renderings. Our proposed model outperforms existing state-of-the-art methods in generating 3D assets with polished surfaces and photorealistic textures. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary We created a new computer program called DreamPolish that can turn text into 3D images. It’s good at making the shapes and details look nice and realistic. The program uses special techniques to make sure the shapes are correct and the details are accurate. It also has a special way of making the textures (the patterns on the surface) look realistic. Our tests showed that DreamPolish is better than other programs at doing this. |
Keywords
» Artificial intelligence » Distillation