Summary of Retrieval-augmented Score Distillation For Text-to-3d Generation, by Junyoung Seo et al.
Retrieval-Augmented Score Distillation for Text-to-3D Generation
by Junyoung Seo, Susung Hong, Wooseok Jang, Inès Hyeonsu Kim, Minseop Kwak, Doyup Lee, Seungryong Kim
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to improve the quality and consistency of 3D scenes generated from text descriptions, known as text-to-3D generation. The current state-of-the-art models rely on powerful 2D diffusion models but struggle with inconsistent 3D geometry due to limited 3D prior knowledge. To address this issue, the authors introduce a retrieval-augmented approach called ReDream, which leverages semantically relevant assets to incorporate their geometric prior into the optimization process. This allows for both expressiveness of 2D diffusion models and geometric consistency of 3D assets to be fully utilized. The proposed framework demonstrates superior quality with increased geometric consistency compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Text-to-3D generation is a way to create 3D scenes from written descriptions. Right now, this technology isn’t very good at creating consistent 3D shapes because it relies too much on 2D information and not enough on 3D details. To fix this problem, the authors created a new approach called ReDream that uses relevant 3D data to help improve the quality of generated scenes. This means that the 3D scenes will be more realistic and consistent in terms of their shapes and structures. |
Keywords
* Artificial intelligence * Diffusion * Optimization
