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.

翻译：程序化内容生成（PCG）以及基于机器学习的程序化内容生成（PCGML）已在先前工作中被用于生成各类游戏的关卡。本文引入内容增强的概念，并聚焦于关卡修补这一子问题，其涉及重建与扩展电子游戏关卡。受图像修补技术的启发，我们对该领域的两项技术进行适配，以应对我们的特定应用场景。我们提出了两种关卡修补方法：自编码器与U形网络。通过一项综合性案例研究，我们证明了这两种方法相较于基线方法具有更优性能，并探讨了其相对优势。此外，我们还针对关卡修补任务对两种方法进行了实践性演示，并为未来研究方向提供了见解。