让AI生成游戏世界，还能实时操控角色动作
现在的AI生成视频，你只能看，不能动。这篇把游戏逻辑和画面生成拆开：先用骨架和几何信息把角色动作“翻译”成AI能懂的结构，再让AI照着画，同时用一个自适应遮罩机制保证动作和画面不跑偏。结果是你可以在750个场景、178个角色里零样本切换，实时指挥角色做新动作，画面还保持连贯。它不是你明天就能玩上的游戏，但这是“AI当游戏引擎”而不是“AI当画师”的第一步。
📄 原文摘要(英文)
Recent advances in video world models have enabled the generation of increasingly realistic and interactive virtual environments, opening new possibilities for player-customizable gameplay. Agentic frameworks further advance this direction by decoupling explicit game logic and controllable world representations from visual generation. However, existing approaches support only simple game settings and predefined motion patterns, generalize poorly to unseen environments and characters, and offer limited real-time interactivity. In this paper, we introduce Wizard, an agentic game world model that enables real-time, precise control over character motions while generalizing zero-shot across novel scenes, characters, and actions. To achieve this, we construct a diverse dataset spanning 750 scenes and 178 characters, incorporating skeleton and geometry information as structured representations. We leverage these representations as intermediate proxies to condition a video generation model, together with an adaptive masking mechanism that improves alignment between the conditioning signals and generated video rollouts. Furthermore, we introduce an agentic framework that explicitly maintains and updates game states, allowing players to freely configure their virtual worlds and exercise fine-grained control over character motions while preserving visual quality and gameplay consistency. By combining agentic control with structured proxy-based generation, our framework unlocks new possibilities for player-driven game creation with real-time, generalizable, and mechanically consistent world simulation.