Summary of Ice-g: Image Conditional Editing Of 3d Gaussian Splats, by Vishnu Jaganathan et al.
ICE-G: Image Conditional Editing of 3D Gaussian Splats
by Vishnu Jaganathan, Hannah Hanyun Huang, Muhammad Zubair Irshad, Varun Jampani, Amit Raj, Zsolt Kira
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 paper introduces a novel approach to quickly edit 3D models from a single reference view, addressing the limitations of existing methods. The technique segments the edit image and matches semantically corresponding regions across chosen views using DINO features. This enables color or texture changes to be applied to other views in a semantically sensible manner. The edited views serve as an updated dataset for further training and re-styling the 3D scene, producing an edited 3D model with fine-grained control. The framework supports various editing tasks, including local edits, style transfer from example images, and combination of different styles. The method uses Gaussian Splats as a primary 3D representation due to its speed and ease of local editing, but can also work with NeRFs. The paper demonstrates the effectiveness of this approach through multiple examples, achieving higher quality results while offering fine-grained control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to edit 3D models from a single picture. Current methods are slow or compromise on quality, so this technique is faster and better. It works by matching parts of the image to similar parts in other views. This allows changes to be made to one view and then automatically applied to other views. The result is an edited 3D model that can be used as a starting point for further editing or training. The method can be used for many different tasks, such as changing the color or texture of objects. It’s faster and more accurate than current methods. |
Keywords
» Artificial intelligence » Style transfer
