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arxiv: 2409.01491 · v2 · pith:MG7TBZII · submitted 2024-09-02 · cs.CV · cs.AI

EarthGen: Generating the World from Top-Down Views

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classification cs.CV cs.AI
keywords generationmethoddemonstratenovelsuper-resolutionsystemworldability
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. InfiniteDiffusion: Bridging Learned Fidelity and Procedural Utility for Open-World Terrain Generation

    cs.CV 2025-12 unverdicted novelty 6.0

    InfiniteDiffusion adapts diffusion models to produce infinite, seed-consistent, high-fidelity terrain with procedural-noise-like access and 9x speed over prior methods.