Summary of Generate Any Scene: Evaluating and Improving Text-to-vision Generation with Scene Graph Programming, by Ziqi Gao et al.
Generate Any Scene: Evaluating and Improving Text-to-Vision Generation with Scene Graph Programming
by Ziqi Gao, Weikai Huang, Jieyu Zhang, Aniruddha Kembhavi, Ranjay Krishna
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces Generate Any Scene (GAS), a framework that generates scene graphs representing various visual scenes. This novel approach leverages “scene graph programming” to construct scene graphs of varying complexity from a structured taxonomy of visual elements, objects, attributes, and relations. GAS enables the synthesis of an almost infinite variety of scene graphs and translates each into a caption, allowing for scalable evaluation of text-to-vision models through standard metrics. The paper conducts extensive evaluations across multiple text-to-image, text-to-video, and text-to-3D models, revealing key findings on model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having machines that can create pictures or videos based on text descriptions. This technology has the potential to revolutionize industries like entertainment, education, and marketing. The paper introduces a new framework called Generate Any Scene (GAS) that makes it possible to evaluate these machines in a more meaningful way. GAS generates scene graphs, which are structured representations of visual scenes, and translates them into captions. This allows researchers to test how well the machines can create images or videos that match their descriptions. |
