Video World Models

Video world models predict how scenes change over time, often in response to actions or camera movements, so agents can explore, plan, and test decisions in imagined environments. Common approaches use autoregressive or diffusion video generation, sometimes predicting compact visual features instead of pixels, and add action conditioning, 3D geometry, persistent memory, or explicit world states to improve control and consistency. Applications include robotic manipulation, autonomous driving, navigation, and interactive games, with evaluation increasingly testing physical dynamics, action following, memory, and downstream task performance alongside visual quality. A central challenge is that realistic-looking video does not necessarily preserve hidden state, obey physics, or support reliable long-horizon interaction, while richer simulation and memory can raise compute and latency costs.