Sketch-based terrain generation seeks to create realistic landscapes for virtual environments in various applications such as computer games, animation and virtual reality. Recently, deep learning based terrain generation has emerged, notably the ones based on generative adversarial networks (GAN). However, these methods often struggle to fulfill the requirements of flexible user control and maintain generative diversity for realistic terrain. Therefore, we propose a novel diffusion-based method, namely terrain diffusion network (TDN), which actively incorporates user guidance for enhanced controllability, taking into account terrain features like rivers, ridges, basins, and peaks. Instead of adhering to a conventional monolithic denoising process, which often compromises the fidelity of terrain details or the alignment with user control, a multi-level denoising scheme is proposed to generate more realistic terrains by taking into account fine-grained details, particularly those related to climatic patterns influenced by erosion and tectonic activities. Specifically, three terrain synthesisers are designed for structural, intermediate, and fine-grained level denoising purposes, which allow each synthesiser concentrate on a distinct terrain aspect. Moreover, to maximise the efficiency of our TDN, we further introduce terrain and sketch latent spaces for the synthesizers with pre-trained terrain autoencoders. Comprehensive experiments on a new dataset constructed from NASA Topology Images clearly demonstrate the effectiveness of our proposed method, achieving the state-of-the-art performance. Our code and dataset will be publicly available.

翻译：草图引导的地形生成旨在为计算机游戏、动画和虚拟现实等应用中的虚拟环境创建逼真的地形。近年来，基于深度学习的地形生成方法兴起，尤其是基于生成对抗网络（GAN）的方法。然而，这些方法往往难以满足灵活的用户控制需求，并保持生成地形的多样性。为此，我们提出了一种新颖的基于扩散的方法，即地形扩散网络（TDN），该方法主动融合用户引导以增强可控性，同时考虑河流、山脊、盆地和山峰等地形特征。不同于传统单一的去噪过程（该过程常会牺牲地形细节的保真度或与用户控制的一致性），我们提出了一种多层次去噪方案，通过考虑细粒度细节（尤其是受侵蚀和构造活动影响的气候模式相关细节）生成更逼真的地形。具体地，我们设计了三个地形合成器，分别用于结构级、中间级和细粒度级的去噪，使每个合成器专注于不同的地形方面。此外，为最大化TDN的效率，我们进一步利用预训练的地形自编码器为合成器引入了地形和草图潜在空间。在基于NASA地形图像构建的新数据集上的综合实验清晰证明了我们方法的有效性，达到了当前最优性能。我们的代码和数据集将公开发布。