Adaptive & Multi-Resolution Procedural Infinite Terrain Generation with Diffusion Models and Perlin Noise
Aryamaan Jain, Avinash Sharma, K. S. Rajan
Abstract
This paper proposes a novel adaptive multi-resolution framework for generating terrains. Our framework combines diffusion-based generative network and novel frequency separated terrain features for terrain patch generation. Additionally, we propose to leverage learnable terrain super-resolution for enhancing generated terrain patch followed by novel kernel-based blending of these patches using Perlin noise to generate infinite terrain with realistic terrain features. We provide a comprehensive quantitative and qualitative evaluation of the proposed framework.
Topics & Concepts
TerrainComputer scienceLeverage (statistics)Kernel (algebra)Terrain renderingArtificial intelligenceNoise (video)Computer visionVisualizationGeographyMathematicsImage (mathematics)CartographyCombinatoricsAdvanced Image Processing TechniquesComputer Graphics and Visualization TechniquesAdvanced Vision and Imaging