Summary of Realmdreamer: Text-driven 3d Scene Generation with Inpainting and Depth Diffusion, by Jaidev Shriram et al.
RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion
by Jaidev Shriram, Alex Trevithick, Lingjie Liu, Ravi Ramamoorthi
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 introduces RealmDreamer, a technique for generating 3D scenes from text descriptions. The method optimizes a 3D Gaussian Splatting representation to match complex text prompts using pretrained diffusion models. A key insight is the use of 2D inpainting diffusion models conditioned on an initial scene estimate to provide low variance supervision for unknown regions during 3D distillation, along with geometric distillation from a depth diffusion model. The initialization of the optimization is crucial, and the paper provides a principled methodology for doing so. RealmDreamer doesn’t require video or multi-view data and can synthesize various high-quality 3D scenes in different styles with complex layouts. It even allows 3D synthesis from a single image. The method outperforms all existing approaches, preferred by 88-95% as measured by a comprehensive user study. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to make 3D pictures from text descriptions. It uses special computer models to take the text and turn it into a 3D scene. This is important because it can help us create new 3D worlds for video games, movies, or even architecture. The method doesn’t need a lot of data, just some initial information about what the 3D scene should look like. It’s also very good at creating different styles and layouts. People who tested the method really liked it, saying it was better than all other approaches. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Distillation » Optimization
