MultiWorld: Interactive Real-Time Multi-Agent Multi-View Video World Models

Video world models have achieved remarkable success in simulating environmental dynamics in response to users' actions. 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 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 Agent Identity Embedding to achieve precise multi-agent controllability, and Dynamic Geometry World State to ensure coherent observations across different views. We employ Causal Teacher Distillation for interactive deployment. 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. Our few-step Multiworld supports 24 FPS generation for both view.