Summary of Disentangled 3d Scene Generation with Layout Learning, by Dave Epstein et al.
Disentangled 3D Scene Generation with Layout Learning
by Dave Epstein, Ben Poole, Ben Mildenhall, Alexei A. Efros, Aleksander Holynski
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 a novel approach to generating 3D scenes that are disentangled into their component objects. The method relies on a large pretrained text-to-image model and is unsupervised, meaning it doesn’t require any additional training data. The key insight behind the approach is that objects can be discovered by rearranging parts of a 3D scene in different spatial configurations while keeping the overall scene valid. To achieve this, the paper jointly optimizes multiple Neural Radiance Fields (NeRFs) from scratch, each representing an object, along with a set of layouts that combine these objects into scenes. The generated scenes are then encouraged to be indistinguishable from those produced by the image generator. The results show that this simple approach is effective in generating 3D scenes decomposed into individual objects, enabling new capabilities in text-to-3D content creation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making it easier to create 3D scenes that are made up of separate objects. Right now, computers are great at generating entire scenes from scratch, but they can’t break those scenes down into their individual parts. The researchers came up with a new way to do this by using a big computer model that’s already good at understanding images and text. They found that if you take different parts of a 3D scene and rearrange them in new ways, the resulting scenes will still look real. By combining these techniques, they were able to generate 3D scenes that are made up of individual objects, which could be really useful for things like creating virtual reality worlds or generating images from text descriptions. |
Keywords
* Artificial intelligence * Unsupervised
