AI可生成可实时交互的高质量游戏，且持续运行超千帧不失真。
Playable Game Generation
- 基于自回归DiT的扩散模型生成游戏内容与机制
- 在RTX 2060上实现千帧以上稳定实时交互
- 提供可评估可玩性的完整框架，适合游戏开发与AIGC研究者
近年来，人工智能生成内容（AIGC）已从文本到图像发展至文本到视频及多模态视频合成。然而，生成可玩游戏面临实时交互、高视觉质量及准确模拟游戏机制的严苛挑战。现有方法或缺乏实时能力，或无法准确模拟交互机制。为此，我们提出新方法PlayGen，包括游戏数据生成、基于自回归DiT的扩散模型以及综合可玩性评估框架。在经典2D与3D游戏上验证，PlayGen实现实时交互、足够视觉质量，并准确模拟交互机制。值得注意的是，该性能在NVIDIA RTX 2060显卡上持续超过1000帧游戏过程仍保持稳定。代码已公开：https://github.com/GreatX3/Playable-Game-Generation。AI生成的游戏演示地址：http://124.156.151.207。
原文摘要 · Abstract (English)
In recent years, Artificial Intelligence Generated Content (AIGC) has advanced from text-to-image generation to text-to-video and multimodal video synthesis. However, generating playable games presents significant challenges due to the stringent requirements for real-time interaction, high visual quality, and accurate simulation of game mechanics. Existing approaches often fall short, either lacking real-time capabilities or failing to accurately simulate interactive mechanics. To tackle the playability issue, we propose a novel method called \emph{PlayGen}, which encompasses game data generation, an autoregressive DiT-based diffusion model, and a comprehensive playability-based evaluation framework. Validated on well-known 2D and 3D games, PlayGen achieves real-time interaction, ensures sufficient visual quality, and provides accurate interactive mechanics simulation. Notably, these results are sustained even after over 1000 frames of gameplay on an NVIDIA RTX 2060 GPU. Our code is publicly available: https://github.com/GreatX3/Playable-Game-Generation. Our playable demo generated by AI is: http://124.156.151.207.
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