GameTileNet is a dataset designed to provide semantic labels for low-resolution digital game art, advancing procedural content generation (PCG) and related AI research as a vision-language alignment task. Large Language Models (LLMs) and image-generative AI models have enabled indie developers to create visual assets, such as sprites, for game interactions. However, generating visuals that align with game narratives remains challenging due to inconsistent AI outputs, requiring manual adjustments by human artists. The diversity of visual representations in automatically generated game content is also limited because of the imbalance in distributions across styles for training data. GameTileNet addresses this by collecting artist-created game tiles from OpenGameArt.org under Creative Commons licenses and providing semantic annotations to support narrative-driven content generation. The dataset introduces a pipeline for object detection in low-resolution tile-based game art (e.g., 32x32 pixels) and annotates semantics, connectivity, and object classifications. GameTileNet is a valuable resource for improving PCG methods, supporting narrative-rich game content, and establishing a baseline for object detection in low-resolution, non-photorealistic images. TL;DR: GameTileNet is a semantic dataset of low-resolution game tiles designed to support narrative-driven procedural content generation through visual-language alignment.

翻译：GameTileNet是一个专为低分辨率数字游戏美术提供语义标注的数据集，旨在通过视觉-语言对齐任务推动程序化内容生成及相关人工智能研究。大型语言模型和图像生成式AI模型已使独立开发者能够为游戏交互创建视觉资源（如精灵图），但由于AI输出结果的不一致性，生成与游戏叙事相符的视觉内容仍具挑战性，需要人工艺术家进行手动调整。此外，由于训练数据在风格分布上的不均衡，自动生成游戏内容的视觉表现多样性也受到限制。GameTileNet通过收集OpenGameArt.org上基于知识共享许可协议的艺术家创作游戏图块，并提供语义标注以支持叙事驱动的内容生成，从而解决上述问题。该数据集构建了针对低分辨率图块化游戏美术（如32×32像素）的目标检测流程，并对语义信息、连通性及对象分类进行标注。GameTileNet作为改进程序化内容生成方法、支持叙事丰富游戏内容的重要资源，同时为低分辨率非写实图像的目标检测建立了基准。TL;DR：GameTileNet是一个低分辨率游戏图块的语义数据集，旨在通过视觉-语言对齐支持叙事驱动的程序化内容生成。