Reconstructing Existing Levels through Level Inpainting
This addresses content augmentation for game developers, but it is incremental as it adapts existing image inpainting methods to a new domain.
The paper tackles the problem of reconstructing and extending video game levels through level inpainting, introducing two adapted techniques (Autoencoder and U-net) that show superior performance compared to a baseline method.
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.