Summary of Nerf-insert: 3d Local Editing with Multimodal Control Signals, by Benet Oriol Sabat et al.
NeRF-Insert: 3D Local Editing with Multimodal Control Signals
by Benet Oriol Sabat, Alessandro Achille, Matthew Trager, Stefano Soatto
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 NeRF-Insert framework allows for high-quality, locally controlled scene editing by casting it as an in-painting problem that preserves global structure. Unlike previous image-to-image models, this approach enables flexible editing control while accepting a combination of textual and visual inputs, including images, CAD models, and binary masks. The framework lifts local edits to 3D-consistent NeRF edits, demonstrating better visual quality and consistency with the original NeRF compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re editing a 3D scene or game environment. You want to add some new objects or change the lighting, but you don’t have the tools to do it easily. A team of researchers has developed a new way to edit these scenes called NeRF-Insert. It’s like having a superpower that lets you make changes and see how they look from different angles all at once. The best part is that this tool can understand what you want to change by looking at a combination of pictures, 3D models, and even text descriptions. |
