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

Lode Enhancer: Level Co-creation Through Scaling

Designing a logo is a long, complicated, and expensive process for any
designer. However, recent advancements in generative algorithms provide models
that could offer a possible solution. Logos are multi-modal, have very few
categorical properties, and do not have a continuous latent space. Yet,
conditional generative adversarial networks can be used to generate logos that
could help designers in their creative process. We propose LoGAN: an improved
auxiliary classifier Wasserstein generative adversarial neural network (with
gradient penalty) that is able to generate logos conditioned on twelve
different colors. In 768 generated instances (12 classes and 64 logos per
class), when looking at the most prominent color, the conditional generation
part of the model has an overall precision and recall of 0.8 and 0.7
respectively. LoGAN's results offer a first glance at how artificial
intelligence can be used to assist designers in their creative process and open
promising future directions, such as including more descriptive labels which
will provide a more exhaustive and easy-to-use system.

本研究使用条件生成对抗网络生成了基于 12 种不同颜色条件的标志，在 768 个生成的实例中，条件生成部分的精确度和召回率分别为 0.8 和 0.7，这为人工智能能够协助设计师的创作过程以及未来更多方向的研究提供了前瞻性的方向。