论文标题
蟾蜍：一个示例的连贯风格水平生成
TOAD-GAN: Coherent Style Level Generation from a Single Example
论文作者
论文摘要
在这项工作中，我们介绍了Toad-Gan（基于令牌的单发尺寸生成对抗网络），这是一种新型的程序内容生成（PCG）算法，该算法生成基于令牌的视频游戏级别。 Toad-Gan遵循新加坡建筑，只能使用一个例子进行培训。我们演示了其对超级马里奥兄弟级别的应用，并能够以任意大小的新风格生成新的级别。我们在建模训练水平的模式并与几个指标下的不同基线进行比较时，实现了最新的结果。此外，我们提出了该方法的扩展，该方法允许用户控制某些令牌结构的生成过程，以确保连贯的全球级别布局。我们向社区提供此工具，通过发布我们的源代码来刺激进一步的研究。
In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. TOAD-GAN follows the SinGAN architecture and can be trained using only one example. We demonstrate its application for Super Mario Bros. levels and are able to generate new levels of similar style in arbitrary sizes. We achieve state-of-the-art results in modeling the patterns of the training level and provide a comparison with different baselines under several metrics. Additionally, we present an extension of the method that allows the user to control the generation process of certain token structures to ensure a coherent global level layout. We provide this tool to the community to spur further research by publishing our source code.
