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.

通过深度神经网络在基于谜题平台游戏《Lode Runner》的2D游戏关卡中将人工降低分辨率的图块进行放大作为设计辅助工具，我们研究了AI增强放大技术。在一个基于网络的编辑器中，用户可以在4x4、8x8和16x16三个不同分辨率的水平上创建和编辑关卡，这些编辑可以即时应用到其他分辨率上。为了实现更高分辨率图片所需的特征，我们训练了神经网络来模拟这些特征。为了探索该工具的潜力并引导进一步的发展，我们进行了一项定性研究并与3位设计师合作，以了解他们如何使用该工具。设计师们喜欢与该工具共同设计，对其基本概念表示欣赏，并提供了改进的反馈意见。

引领增强：通过规模实现水平共创