Reconstructing Existing Levels through Level Inpainting


  • Johor Jara Gonzalez University of Alberta
  • Matthew Guzdial University of Alberta



Procedural Content Generation, Machine Learning, Level Generation, Image Inpainting


Procedural Content Generation (PCG) and Procedural Content Generation via Machine Learning (PCGML) have been used in prior work for generating levels in various games. This paper introduces Content Augmentation and focuses on the subproblem of level inpainting, which involves reconstructing and extending video game levels. Drawing inspiration from image inpainting, we adapt two techniques from this domain to address our specific use case. We present two approaches for level inpainting: an Autoencoder and a U-net. Through a comprehensive case study, we demonstrate their superior performance compared to a baseline method and discuss their relative merits. Furthermore, we provide a practical demonstration of both approaches for the level inpainting task and offer insights into potential directions for future research.




How to Cite

Jara Gonzalez, J., & Guzdial, M. (2023). Reconstructing Existing Levels through Level Inpainting. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 19(1), 276-283.