Video world models have achieved remarkable success in simulating environmental dynamics in response to actions by users or agents. They are modeled as action-conditioned video generation models that take historical frames and current actions as input to predict future frames. Yet, most existing approaches are limited to single-agent scenarios and fail to capture the complex interactions inherent in real-world multi-agent systems. We present \textbf{MultiWorld}, a unified framework for multi-agent multi-view world modeling that enables accurate control of multiple agents while maintaining multi-view consistency. We introduce the Multi-Agent Condition Module to achieve precise multi-agent controllability, and the Global State Encoder to ensure coherent observations across different views. MultiWorld supports flexible scaling of agent and view counts, and synthesizes different views in parallel for high efficiency. Experiments on multi-player game environments and multi-robot manipulation tasks demonstrate that MultiWorld outperforms baselines in video fidelity, action-following ability, and multi-view consistency. Project page: https://multi-world.github.io/

翻译：视频世界模型在模拟环境对用户或智能体动作的动态响应方面取得了显著成功。这类模型被构建为动作条件视频生成模型，以历史帧和当前动作为输入，预测未来帧。然而，现有方法大多局限于单智能体场景，难以捕捉现实多智能体系统中固有的复杂交互。我们提出**MultiWorld**，一个统一的多智能体多视角世界建模框架，能够在保持多视角一致性的同时实现对多个智能体的精准控制。我们引入多智能体条件模块以实现精确的多智能体可控性，以及全局状态编码器以确保不同视角下观测结果的连贯性。MultiWorld支持灵活扩展智能体与视角数量，并能够并行合成不同视角以实现高效处理。在多玩家游戏环境与多机器人操作任务上的实验表明，MultiWorld在视频保真度、动作跟随能力以及多视角一致性方面均优于基线方法。项目页面：https://multi-world.github.io/