EarthGen: Generating the World from Top-Down Views

Ansh Sharma, Albert Xiao, Praneet Rathi, Rohit Kundu, Albert Zhai +2 more
9/2/2024
cs.CVcs.AI

No official code found. Best implementation: anshgs/earthgen (7 stars, last commit 2024-09-05).

Abstract

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.

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Code Implementations(1)

anshgs/earthgenCommunity100%
70Python, Jupyter NotebookJun 4, 20242 years ago

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Cite this paper

@article{sharma2024earthgen,
  title  = {EarthGen: Generating the World from Top-Down Views},
  author = {Ansh Sharma and Albert Xiao and Praneet Rathi and Rohit Kundu and Albert Zhai and Yuan Shen and Shenlong Wang},
  year   = {2024},
  eprint = {2409.01491},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.01491v2}
}

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