We explore AI-powered upscaling as a design assistance tool in the context of creating 2D game levels. Deep neural networks are used to upscale artificially downscaled patches of levels from the puzzle platformer game Lode Runner. The trained networks are incorporated into a web-based editor, where the user can create and edit levels at three different levels of resolution: 4x4, 8x8, and 16x16. An edit at any resolution instantly transfers to the other resolutions. As upscaling requires inventing features that might not be present at lower resolutions, we train neural networks to reproduce these features. We introduce a neural network architecture that is capable of not only learning upscaling but also giving higher priority to less frequent tiles. To investigate the potential of this tool and guide further development, we conduct a qualitative study with 3 designers to understand how they use it. Designers enjoyed co-designing with the tool, liked its underlying concept, and provided feedback for further improvement.

翻译：我们探索了以人工智能驱动的超分辨率技术作为设计辅助工具在创建2D游戏关卡中的应用。深度神经网络被用于人工降采样的《Lode Runner》解谜平台游戏关卡图块块的超分辨率重建。训练好的网络被集成到一个基于网页的编辑器中，用户可以在三种不同分辨率层级（4x4、8x8和16x16）上创建和编辑关卡。任何分辨率下的编辑操作都会即时同步到其他分辨率。由于超分辨率需要生成低分辨率下可能缺失的特征，我们训练神经网络来复现这些特征。我们提出了一种新颖的神经网络架构，该架构不仅能学习超分辨率，还能对低频出现的图块赋予更高优先级。为探究该工具的潜力并指导后续开发，我们邀请了3位设计师开展质性研究，观察他们的使用方式。设计师们享受与工具的协同创作过程，认可其核心理念，并为进一步改进提供了反馈意见。