InfiniteDiffusion adapts diffusion models to produce infinite, seed-consistent, high-fidelity terrain with procedural-noise-like access and 9x speed over prior methods.
A step towards procedural terrain generation with GANs
1 Pith paper cite this work, alongside 15 external citations. Polarity classification is still indexing.
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abstract
Procedural terrain generation for video games has been traditionally been done with smartly designed but handcrafted algorithms that generate heightmaps. We propose a first step toward the learning and synthesis of these using recent advances in deep generative modelling with openly available satellite imagery from NASA.
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cs.CV 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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InfiniteDiffusion: Bridging Learned Fidelity and Procedural Utility for Open-World Terrain Generation
InfiniteDiffusion adapts diffusion models to produce infinite, seed-consistent, high-fidelity terrain with procedural-noise-like access and 9x speed over prior methods.