CPPN2WFC: Extending Wave Function Collapse to Generate Globally Coherent Content

Procedural content generation (PCG) enables the creation of vast, varied, and aesthetically rich environments with minimal manual effort. One of the most widely used techniques for procedural map generation is Wave Function Collapse (WFC), a constraint-based algorithm that synthesizes game maps by propagating local patterns while ensuring global consistency. However, despite its effectiveness, WFC often produces repetitive structures and lacks the ability to introduce higher-order spatial coherence or emergent design patterns. This paper explores whether combining Compositional Pattern Producing Networks (CPPN) and WFC - a hybrid method we term CPPN2WFC - leads to more structured and visually compelling game maps compared to using WFC or CPPNs alone. CPPNs, which are artificial neural networks with a selection of different activation functions, have been shown to generate intricate patterns and organic-like structures when evolved through NEAT, a method that dynamically evolves both network topology and weights over generations. By integrating CPPNs into the WFC framework, we introduce an additional layer of flexibility, allowing both constraint satisfaction and high-level structural control. We conduct comparative experiments through an Interactive Evolutionary interface and user study. Main results show that compared to CPPNs or WFC alone, CPPN2WFC strikes a balance between producing global and local patterns.