Controllable Video Generation

The field studies how to make generated videos follow user intent, from preserving a subject or style to specifying poses, object trajectories, camera moves, events, and physical properties. Common approaches condition diffusion models with text, images, tracks, masks, or 3D geometry, adapt pretrained models with ControlNet or LoRA modules, or steer sampling at inference; newer world models add explicit state, planning, and memory for interactive, long-horizon generation. Evaluation spans prompt and control adherence, identity and scene consistency, motion accuracy, physical plausibility, and video quality, including tests on driving, human-object interaction, and cinematic scenes. A central tension remains: stronger or denser controls can improve precision but may reduce naturalness or visual quality, while long rollouts can drift or fail to realize intended state changes even when individual frames look convincing.