@article{Awiszus_Schubert_Rosenhahn_2020, title={TOAD-GAN: Coherent Style Level Generation from a Single Example}, volume={16}, url={https://ojs.aaai.org/index.php/AIIDE/article/view/7401}, DOI={10.1609/aiide.v16i1.7401}, abstractNote={<p class="abstract">In this work, we present <strong>TOAD-GAN</strong> (<strong>T</strong>oken-based <strong>O</strong>ne-shot <strong>A</strong>rbitrary <strong>D</strong>imension <strong>G</strong>enerative <strong>A</strong>dversarial <strong>N</strong>etwork), 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 <em>Super Mario Bros.</em> 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.</p>}, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment}, author={Awiszus, Maren and Schubert, Frederik and Rosenhahn, Bodo}, year={2020}, month={Oct.}, pages={10-16} }