Streaming Video Generation

Streaming video generation studies how to produce video a frame or chunk at a time so users can interact with it before the full sequence is complete. Most approaches adapt diffusion transformers to causal autoregressive generation, then use few-step distillation and self-forcing to reduce latency; rolling key-value caches and learned memory help retain useful context. Applications include interactive world models, talking avatars, and live video editing, with quality evaluated on measures such as VBench and long-horizon consistency benchmarks. The central tension is balancing real-time responsiveness and bounded memory with visual fidelity, motion quality, and coherence over long sequences, since causal generation can lose information available to slower bidirectional models.