Translations:Diffusion Models Are Real-Time Game Engines/92/zh: Difference between revisions

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'''Towards a New Paradigm for Interactive Video Games.''' Today, video games are ''programmed'' by humans. ''GameNGen'' is a proof-of-concept for one part of a new paradigm where games are weights of a neural model, not lines of code. ''GameNGen'' shows that an architecture and model weights exist such that a neural model can effectively run a complex game (DOOM) interactively on existing hardware. While many important questions remain, we are hopeful that this paradigm could have important benefits. For example, the development process for video games under this new paradigm might be less costly and more accessible, whereby games could be developed and edited via textual descriptions or example images. A small part of this vision, namely creating modifications or novel behaviors for existing games, might be achievable in the shorter term. For example, we might be able to convert a set of frames into a new playable level or create a new character just based on example images, without having to author code. Other advantages of this new paradigm include strong guarantees on frame rates and memory footprints. We have not experimented with these directions yet and much more work is required here, but we are excited to try! Hopefully, this small step will someday contribute to a meaningful improvement in people’s experience with video games, or maybe even more generally, in day-to-day interactions with interactive software systems.

迈向互动视频游戏的新范式 如今，视频游戏是由人类编程的。而GameNGen则是新范式的一部分概念验证，在这一新范式中，游戏被视为神经模型的权重，而不是代码行。GameNGen展示了一种架构和模型权重，使神经模型能够有效地在现有硬件上互动运行复杂的游戏（如 DOOM）。虽然仍有许多重要问题，但我们希望这种范式能带来显著益处。例如，在这种新范式下，视频游戏的开发过程可能成本更低且更易于获取，可以通过文字描述或示例图像来开发和编辑游戏。这个愿景的一小部分，即对现有游戏进行修改或创造新的行为，也许在短期内就能实现。例如，我们也许可以将一组帧转换成一个新的可玩关卡，或根据示例图像创建一个新角色，而无需编写代码。这种新范式的其他优点还包括对帧率和内存占用的强有力保证。我们尚未在这些方向上进行实验，并且还需要做更多的工作，但我们很高兴尝试！希望有朝一日，这一小步能为改善人们的视频游戏体验做出重大贡献，甚至更广泛地说，改善人们与互动软件系统的日常交互体验。