Summary of Magic-boost: Boost 3d Generation with Multi-view Conditioned Diffusion, by Fan Yang et al.
Magic-Boost: Boost 3D Generation with Multi-View Conditioned Diffusion
by Fan Yang, Jianfeng Zhang, Yichun Shi, Bowen Chen, Chenxu Zhang, Huichao Zhang, Xiaofeng Yang, Xiu Li, Jiashi Feng, Guosheng Lin
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers leverage advancements in 2D diffusion models to improve the generation of 3D content. They propose a multi-view based refinement method called Magic-Boost, which enhances the quality of generated 3D assets by incorporating strong 3D priors extracted from synthesized multi-view images. This approach enables precise optimization guidance for refining coarse-generated results, resulting in improved local details and textures within a relatively short time frame. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve the generation of 3D content using pre-trained 2D diffusion models. The team developed Magic-Boost, a novel method that refines generated 3D assets by incorporating strong 3D priors from synthesized multi-view images. This helps optimize coarse-generated results for better geometry and texture details within a short time. The result is high-quality 3D assets with improved local detail. |
Keywords
» Artificial intelligence » Diffusion » Optimization
