ShotStream: Real-Time Multi-Shot Video Generation

机器之心 · wechat · 2026-07-11

## ShotStream: Real-Time Streaming Multi-Shot Video Generation The CUHK MMLab and Kuaishou Kling team proposed **ShotStream**, a real-time streaming multi-shot generation framework designed for **interactive long video creation**. The authors point out that traditional methods for multi-shot long videos typically suffer from high latency and poor interactivity: users often have to submit the entire script and storyboard at once, and a single failed shot can ruin the whole generation process. ## Key Innovations - **Reformulating multi-shot synthesis as "next-shot generation based on historical context,"** allowing users to adjust the narrative in real-time via dynamic streaming prompts during generation. - **Dual-cache memory mechanism:** - GlobalCache ensures cross-shot consistency; - LocalCache maintains fluidity within a single shot. - To avoid temporal ambiguity caused by the dual cache, the authors introduced **RoPE residual/discontinuous position encoding** to distinguish between caches. - Proposed a **two-stage Self-Forcing distillation strategy** to mitigate error accumulation in long video autoregressive generation. ## Performance & Implementation - Achieved **16 FPS** inference speed on a **single H200 GPU**. - Improved generation efficiency by **more than 25x** compared to bidirectional models. - The paper has been accepted by **ECCV 2026**. - Training, testing code, and models are open-sourced. The article also adds experimental conclusions: ShotStream outperforms or closely matches existing bidirectional and autoregressive methods in metrics like cross-shot consistency, shot transition control, and text alignment.

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