Autoregressive Video Generation

Autoregressive video generation studies how to produce a sequence of frames or video chunks conditioned on earlier content, enabling streaming output and interactive control. Common approaches use causal diffusion transformers, often distilled from bidirectional models to reduce denoising steps, and add action, camera, audio, or text conditioning for world models and editing. To extend generation beyond short clips, methods train on model-generated histories and use retrieval, compressed KV caches, or structured memory to preserve identities, scenes, and motion. The central challenge is balancing speed and bounded memory with visual quality, diverse motion, and consistency over long rollouts, which benchmarks such as VBench-Long and task-specific interaction tests attempt to measure.