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.

基于草图的地形生成方法中，我们提出了一种新的扩散网络方法，即地形扩散网络（TDN），它通过主动融入用户引导来增强可控性，同时考虑到河流、山脊、盆地和山峰等地形特征，生成更加逼真的地形。通过针对结构、中间和细粒度级别的多层去噪方案，以及引入预训练的地形自编码器的地形和草图潜空间，我们的方法在基于NASA地形图像构建的新数据集上进行了广泛实验证明了其有效性，达到了最先进的性能水平。

地形扩散网络: 基于地质素描引导的气候感知地形生成