Summary of Earthgen: Generating the World From Top-down Views, by Ansh Sharma et al.
EarthGen: Generating the World from Top-Down Views
by Ansh Sharma, Albert Xiao, Praneet Rathi, Rohit Kundu, Albert Zhai, Yuan Shen, Shenlong Wang
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed method for extensive multi-scale generative terrain modeling leverages a cascade of superresolution diffusion models to produce consistent images across multiple resolutions. This approach enables the scalable generation of thousands of square kilometers of realistic Earth surfaces at high resolution, outperforming super-resolution baselines in extreme zoom scenarios (up to 1024x). The method also demonstrates its ability to create diverse and coherent scenes via interactive gigapixel-scale generated maps. Furthermore, it can be extended for novel content creation applications such as controllable world generation and 3D scene generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to generate realistic images of Earth’s surface from different scales. They combined multiple super-resolution models to create detailed and accurate images. Their method is very good at generating large areas, like thousands of square kilometers, and can even zoom in extremely close (up to 1024x). The generated maps are also diverse and make sense. This technology has many potential uses, such as creating realistic game worlds or allowing users to interact with their own custom-made 3D scenes. |
Keywords
» Artificial intelligence » Diffusion » Super resolution
