Summary of Dreamcatalyst: Fast and High-quality 3d Editing Via Controlling Editability and Identity Preservation, by Jiwook Kim et al.
DreamCatalyst: Fast and High-Quality 3D Editing via Controlling Editability and Identity Preservation
by Jiwook Kim, Seonho Lee, Jaeyo Shin, Jiho Choi, Hyunjung Shim
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); 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 presents a novel framework called DreamCatalyst for text-driven 3D editing tasks, leveraging diffusion models to achieve high-quality results while reducing training times. The authors identify the root cause of performance degradation in existing methods as a conflict between score distillation sampling (SDS) and diffusion model dynamics. They propose an optimization process that approximates the diffusion reverse process, aligning with diffusion sampling dynamics. This approach enables faster and higher-quality editing compared to state-of-the-art NeRF editing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DreamCatalyst is a new way to edit 3D images using text. Currently, this process takes a long time and doesn’t always produce great results. The researchers found that the problem lies in how existing methods use diffusion models to edit 3D images. They developed DreamCatalyst to fix this issue, making the editing process faster and better. There are two modes: one that is fast but still produces good results, and another that takes a bit longer but produces even better results. This new method outperforms existing methods in both speed and quality. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Distillation » Optimization
