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，程序化生成世界一直建立在Perlin噪声等程序化噪声函数基础上，这类函数虽具有快速生成和无限扩展的特性，但其真实感与大规模连贯性存在根本局限。我们提出的地形扩散模型（Terrain Diffusion）作为Perlin噪声在AI时代的技术继任者，将扩散模型的逼真度与程序化噪声不可或缺的核心特性——无缝无限延展、种子一致性和恒定时间随机访问——完美结合。其核心创新无限扩散算法（InfiniteDiffusion）实现了无边景观的无缝实时生成。通过采用分层堆叠的扩散模型架构，系统实现了行星级宏观环境与局部细节的耦合，而紧凑的拉普拉斯编码则确保了地球级动态范围内的输出稳定性。配套开源的无限张量框架支持对无界张量的恒定内存操作，结合少步长一致性蒸馏技术实现了高效生成。这些技术要素共同确立了扩散模型作为程序化世界生成的实践基础，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.