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This work introduces World-GAN, the first method to perform data-drivenProcedural Content Generation via Machine Learning in Minecraft from a singleexample. Based on a 3D Generative Adversarial Network (GAN) architecture, weare able to create arbitrarily sized world snippets from a given sample. Weevaluate our approach on creations from the community as well as structuresgenerated with the Minecraft World Generator. Our method is motivated by thedense representations used in Natural Language Processing (NLP) introduced withword2vec [1]. The proposed block2vec representations make World-GAN independentfrom the number of different blocks, which can vary a lot in Minecraft, andenable the generation of larger levels. Finally, we demonstrate that changingthis new representation space allows us to change the generated style of analready trained generator. World-GAN enables its users to generate Minecraftworlds based on parts of their creations.

World-GAN: a Generative Model for Minecraft Worlds