Summary of Sin-nerf2nerf: Editing 3d Scenes with Instructions Through Segmentation and Inpainting, by Jiseung Hong et al.
SIn-NeRF2NeRF: Editing 3D Scenes with Instructions through Segmentation and Inpainting
by Jiseung Hong, Changmin Lee, Gyusang Yu
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a method called in2n that enables selective 3D object editing by disentangling it from the background scene. The approach uses text prompts to edit Neural Radiance Field (NeRF) scenes, but existing methods struggle with simultaneous geometric modifications on both objects and backgrounds. To address this challenge, the authors develop a technique for segmenting objects and inpainting backgrounds, allowing for various operations like resizing or moving disentangled objects within 3D space. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re playing with Legos, and you want to move a specific toy car without affecting the rest of the scenery. This paper makes it possible! They created a way to “cut out” objects from their background scene using text prompts, allowing for precise editing like shrinking or moving objects within 3D space. |
