  This work introduces World-GAN, the first method to perform data-driven
Procedural Content Generation via Machine Learning in Minecraft from a single
example. Based on a 3D Generative Adversarial Network (GAN) architecture, we
are able to create arbitrarily sized world snippets from a given sample. We
evaluate our approach on creations from the community as well as structures
generated with the Minecraft World Generator. Our method is motivated by the
dense representations used in Natural Language Processing (NLP) introduced with
word2vec [1]. The proposed block2vec representations make World-GAN independent
from the number of different blocks, which can vary a lot in Minecraft, and
enable the generation of larger levels. Finally, we demonstrate that changing
this new representation space allows us to change the generated style of an
already trained generator. World-GAN enables its users to generate Minecraft
worlds based on parts of their creations.
