Summary of Make-your-3d: Fast and Consistent Subject-driven 3d Content Generation, by Fangfu Liu et al.
Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation
by Fangfu Liu, Hanyang Wang, Weiliang Chen, Haowen Sun, Yueqi Duan
First submitted to arxiv on: 14 Mar 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 The proposed Make-Your-3D method enables personalized 3D content generation from a single image of a subject and text description within 5 minutes. This novel approach harmonizes the distributions of multi-view diffusion models and identity-specific generative models, aligning them with the desired 3D subject. The co-evolution framework reduces variance through identity-aware optimization and subject-prior optimization. Experimental results demonstrate high-quality, consistent, and subject-specific 3D content generation with text-driven modifications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to create customized 3D objects from just one picture of the object and some written information about it. This method is called Make-Your-3D, and it can produce high-quality results in just 5 minutes. The key idea is to make sure all the different models working together are talking about the same thing – the desired 3D subject. By letting these models learn from each other, the resulting 3D object is personalized and consistent with the original image. |
Keywords
* Artificial intelligence * Diffusion * Optimization
