WaveFunctionCollapse: Content Generation via Constraint Solving and Machine Learning

We describe WaveFunctionCollapse (WFC), a new family of algorithms for content generation. WFC was recently invented by independent game developer Maxim Gumin and has since been adopted and adapted by other game developers. Trends in academic research on content generation have only recently suggested the use of ideas from constraint solving and machine learning, so it is surprising to see these manifested in in-the-wild algorithms developed outside of an academic context. We illuminate the common components in this family of algorithms by way of a rational reconstruction. Through experiments with the reconstruction we probe the impact of design choices made in various adaptations of WFC (e.g. the role of backtracking, search heuristics, or pattern learning strategies). This work highlights a mode of incremental content generation that has been overlooked by past surveys of content generation methods.