Procedural Content Generation via Generative Artificial Intelligence
Xinyu MaoWanli YuYuya OkawaraXueying ZhanKazunori D. YamadaMichael R. Zielewski
Sep 2026
Artificial IntelligenceMachine Learning
Abstract
The attempt to utilize machine learning in procedural content generation (PCG) has been made in the past. In this survey paper, we investigate how generative artificial intelligence (AI), which saw a significant increase in interest in the mid-2010s, is being used for PCG. We review applications of generative AI for the creation of various types of content, including terrains, items, and even storylines. While generative AI is effective for PCG, building high-performance models requires not only handling customized content and ensuring quality and diversity, but also securing sufficient training data. For PCG research to advance further, addressing these challenges is essential. Thus, we also give special consideration to research that explores innovative generation techniques, model architectures, and approaches suited for limited-data scenarios.
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