Procedural terrain generation with style transfer
Fabio Merizzi
In this study we introduce a new technique for the generation of terrain
maps, exploiting a combination of procedural generation and Neural Style
Transfer. We consider our approach to be a viable alternative to competing
generative models, with our technique achieving greater versatility, lower
hardware requirements and greater integration in the creative process of
designers and developers. Our method involves generating procedural noise maps
using either multi-layered smoothed Gaussian noise or the Perlin algorithm. We
then employ an enhanced Neural Style transfer technique, drawing style from
real-world height maps. This fusion of algorithmic generation and neural
processing holds the potential to produce terrains that are not only diverse
but also closely aligned with the morphological characteristics of real-world
landscapes, with our process yielding consistent terrain structures with low
computational cost and offering the capability to create customized maps.
Numerical evaluations further validate our model's enhanced ability to
accurately replicate terrain morphology, surpassing traditional procedural
methods.