地形扩散：基于扩散模型的无限实时地形生成技术——柏林噪声的革新继任者

Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise in Infinite, Real-Time Terrain Generation

December 9, 2025

作者: Alexander Goslin

cs.AI

摘要

数十年来，程序化世界构建一直依赖于Perlin噪声等程序化噪声函数。这类函数虽具有快速生成和无限延展的特性，但在真实感与大规模连贯性方面存在根本局限。我们提出的Terrain Diffusion技术作为Perlin噪声在AI时代的新一代替代方案，将扩散模型的高保真度与程序化噪声的核心优势——无缝无限延伸、种子一致性和恒定时间随机访问——完美结合。其核心创新InfiniteDiffusion算法实现了无限生成机制，可实时无缝合成无边界地形景观。通过采用分层扩散模型架构，系统将行星级宏观环境与局部细节相耦合，同时紧凑的拉普拉斯编码确保在地球尺度的动态范围内保持输出稳定性。配套的开源无限张量框架支持对无界张量进行恒定内存操作，而少步数一致性蒸馏技术则实现了高效生成。这些技术共同确立了扩散模型作为程序化世界生成的新基石，能够以连贯可控的方式无限制地合成整个行星地貌。

English

For decades, procedural worlds have been built on procedural noise functions such as Perlin noise, which are fast and infinite, yet fundamentally limited in realism and large-scale coherence. We introduce Terrain Diffusion, an AI-era successor to Perlin noise that bridges the fidelity of diffusion models with the properties that made procedural noise indispensable: seamless infinite extent, seed-consistency, and constant-time random access. At its core is InfiniteDiffusion, a novel algorithm for infinite generation, enabling seamless, real-time synthesis of boundless landscapes. A hierarchical stack of diffusion models couples planetary context with local detail, while a compact Laplacian encoding stabilizes outputs across Earth-scale dynamic ranges. An open-source infinite-tensor framework supports constant-memory manipulation of unbounded tensors, and few-step consistency distillation enables efficient generation. Together, these components establish diffusion models as a practical foundation for procedural world generation, capable of synthesizing entire planets coherently, controllably, and without limits.