EarthGen: Generating the World from Top-Down Views
Sharma, Ansh, Xiao, Albert, Rathi, Praneet, Kundu, Rohit, Zhai, Albert, Shen, Yuan, Wang, Shenlong
–arXiv.org Artificial Intelligence
In this work, we present a novel method for extensive multi-scale generative terrain modeling. At the core of our model is a cascade of superresolution diffusion models that can be combined to produce consistent images across multiple resolutions. Pairing this concept with a tiled generation method yields a scalable system that can generate thousands of square kilometers of realistic Earth surfaces at high resolution. We evaluate our method on a dataset collected from Bing Maps and show that it outperforms super-resolution baselines on the extreme super-resolution task of 1024x zoom. We also demonstrate its ability to create diverse and coherent scenes via an interactive gigapixel-scale generated map. Finally, we demonstrate how our system can be extended to enable novel content creation applications including controllable world generation and 3D scene generation.
arXiv.org Artificial Intelligence
Sep-7-2024
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