Leibniz University Hannover Proposes World-GAN: A 3D GAN for Minecraft Level Generation

The various levels, quests and characters in modern video games play a major role in these games' engagement and entertainment values. One way to keep things fresh is procedural content generation (PCG), the algorithmic generation of game content using a random process that can produce an unpredictable range of possible gameplay spaces, freeing human game designers from the laborious task of manual content generation. Recent improvements in machine learning (ML) have spurred interest in applying such techniques to PCG, but research on level generation in 3D games remains limited. In the popular 3D Minecraft game, for example, humans still play a central role in content generation -- structures have to be placed manually in a fixed world because the Minecraft World Generator can't generate new structures on its own. To fill the gap between the Minecraft World Generator's PCG and manually created custom structures, a research team from Leibniz University Hannover recently introduced World-GAN, a 3D generative adversarial network (GAN) that can learn and generate structures directly in the Minecraft 3D voxel space.