  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.

Computer Vision and Pattern Recognition;Artificial Intelligence;Multimedia