Summary of Decollage: 3d Detailization by Controllable, Localized, and Learned Geometry Enhancement, By Qimin Chen et al.
DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned Geometry Enhancement
by Qimin Chen, Zhiqin Chen, Vladimir G. Kim, Noam Aigerman, Hao Zhang, Siddhartha Chaudhuri
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 machine learning-based 3D modeling method that enables users to refine or detailize 3D shapes using AI-assisted 3D content creation. The method builds upon a Pyramid GAN, making it masking-aware, and incorporates novel structural losses and priors to preserve coarse structures and fine-grained features. This allows for localized detailization, enabling novel interactive creative workflows and applications. Experimental results demonstrate the superiority of this approach over prior global detailization techniques, producing high-resolution stylized geometries with coherent shape details and style transitions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses AI to help people create 3D shapes in a new way. It’s like coloring book pages for your own creations! You start with a simple shape, then you can add more details by “painting” them on using examples of what you want the final shape to look like. The AI takes these examples and turns them into high-quality detailed designs that match the style you chose. This technology has many possibilities for creative projects and making things in 3D. |
Keywords
» Artificial intelligence » Gan » Machine learning
