Toward Natural Language Based PCG in Minecraft

This article presents Wor(l)d-GAN, a method to perform data-driven procedural content generation via machine learning in Minecraft from a single example. Based on a 3-D generative adversarial network (GAN) architecture, we are able to create arbitrarily sized world snippets from a given sample. Our method applies dense representations used in natural language processing in two ways. First, we propose block2vec representations based on word2vec . Second, we use the pretrained large language model bidirectional encoder representations from transformers (BERT) to generate representations directly from the token names. These representations make Wor(l)d-GAN independent of the number of different blocks, which can vary a lot in Minecraft , and enable the generation of larger levels. We evaluate our approach on creations from the community as well as structures generated with the Minecraft World Generator under several metrics. Wor(l)d-GAN enables its users to generate Minecraft worlds based on parts of their creations.