Autoregressive video generation treats earlier output as context for producing what follows. A director layer can summarize recent scenes, accept new instructions, and choose the prompt or conditions for the next clip in an ongoing sequence.
The approach supports open-ended streams, but errors and style changes can accumulate as the sequence grows. Context limits, selective memory, consistency checks, and occasional deliberate resets can help control drift.
ELI5
Autoregressive video generation creates what comes next by using the video or context already produced. Each new part depends on the earlier sequence, so the system can continue a scene or story over time.
For example, a model can generate one short scene, summarize what just happened and use that context to make the next clip. This supports longer streams, but small mistakes in characters, style or events can build up, so the system may need consistency checks or a deliberate reset.
