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In this work, we present TOAD-GAN (Token-based One-shot Arbitrary DimensionGenerative Adversarial Network), a novel Procedural Content Generation (PCG)algorithm that generates token-based video game levels. TOAD-GAN follows theSinGAN architecture and can be trained using only one example. We demonstrateits application for Super Mario Bros. levels and are able to generate newlevels of similar style in arbitrary sizes. We achieve state-of-the-art resultsin modeling the patterns of the training level and provide a comparison withdifferent baselines under several metrics. Additionally, we present anextension of the method that allows the user to control the generation processof certain token structures to ensure a coherent global level layout. Weprovide this tool to the community to spur further research by publishing oursource code.

TOAD-GAN: Coherent Style Level Generation from a Single Example