The various levels, quests and characters in modern video games play a major role in these games' engagement and entertainment values. One way to keep things fresh is procedural content generation (PCG), the algorithmic generation of game content using a random process that can produce an unpredictable range of possible gameplay spaces, freeing human game designers from the laborious task of manual content generation. Recent improvements in machine learning (ML) have spurred interest in applying such techniques to PCG, but research on level generation in 3D games remains limited. In the popular 3D Minecraft game, for example, humans still play a central role in content generation -- structures have to be placed manually in a fixed world because the Minecraft World Generator can't generate new structures on its own. To fill the gap between the Minecraft World Generator's PCG and manually created custom structures, a research team from Leibniz University Hannover recently introduced World-GAN, a 3D generative adversarial network (GAN) that can learn and generate structures directly in the Minecraft 3D voxel space.

In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. TOAD-GAN follows the SinGAN architecture and can be trained using only one example. We demonstrate its application for Super Mario Bros. levels and are able to generate new levels of similar style in arbitrary sizes. We achieve state-of-the-art results in modeling the patterns of the training level and provide a comparison with different baselines under several metrics. Additionally, we present an extension of the method that allows the user to control the generation process of certain token structures to ensure a coherent global level layout. We provide this tool to the community to spur further research by publishing our source code.