Summary of Inserf: Text-driven Generative Object Insertion in Neural 3d Scenes, by Mohamad Shahbazi et al.
InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes
by Mohamad Shahbazi, Liesbeth Claessens, Michael Niemeyer, Edo Collins, Alessio Tonioni, Luc Van Gool, Federico Tombari
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
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 InseRF, a novel method for generative object insertion in NeRF reconstructions of 3D scenes, is introduced. Building on textual descriptions and 2D bounding boxes in reference viewpoints, InseRF generates new objects in 3D scenes. This study addresses the challenge of generating new objects in 3D scene editing, which existing methods struggle with. The proposed approach grounds 3D object insertion to a 2D object insertion in a reference view, lifted to 3D using single-view object reconstruction and guided by monocular depth estimation priors. InseRF is evaluated on various 3D scenes, demonstrating effectiveness compared to existing methods. This method enables controllable and 3D-consistent object insertion without requiring explicit 3D information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary InseRF is a new way to add objects to 3D scenes using text descriptions and a 2D picture of what the object should look like from one side. Currently, it’s hard to add new objects to 3D scenes, but InseRF makes it easier by breaking down the task into smaller steps. It starts with adding an object to a 2D picture, then lifts that object to 3D space using another technique. This method is good at adding objects without requiring detailed information about the 3D scene. |
Keywords
* Artificial intelligence * Depth estimation
