Summary of Flow Score Distillation For Diverse Text-to-3d Generation, by Runjie Yan et al.
Flow Score Distillation for Diverse Text-to-3D Generation
by Runjie Yan, Kailu Wu, Kaisheng Ma
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper presents a novel approach to text-to-3D generation, building upon recent advancements in Score Distillation Sampling (SDS) and Denoise Diffusion Implicit Models (DDIM). By reformulating the SDS loss using PF-ODE, the authors show that SDS can be viewed as a generalized DDIM generation process. The study highlights the limitations of current noise sampling strategies in DDIM models, which restrict diversity in generated outcomes. To address this, the authors introduce Flow Score Distillation (FSD), a new text-to-3D method that enhances diversity without compromising quality. Experimental results demonstrate FSD’s effectiveness across various diffusion models and tasks. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to turn text into 3D images. It starts by looking at how SDS works and finds a way to use it in a different way with DDIM. The authors show that this new approach can make 3D images more diverse without making them worse. They also introduce a new method called FSD, which helps to improve the diversity of generated 3D images. |
Keywords
» Artificial intelligence » Diffusion » Distillation