视频编辑也能实时直播了
你刷短视频时，有没有想过直接对着直播画面实时改内容？现在有人做到了。这篇研究搞了个视频编辑框架，能一帧一帧地实时改视频，背景和没改的部分还不会乱跳。关键是一套三阶段蒸馏技术：先让一个强大的双向模型学会怎么改，再把能力压缩给一个单向的实时模型，最后用缓存机制省掉重复计算。实测速度达到12.66帧每秒，已经能用于AR眼镜那种实时交互场景。虽然离你手机里直接用还有距离，但直播带货、视频会议里实时换背景或加特效，可能很快就能实现了。
📄 原文摘要(英文)
Streaming video editing has made rapid progress, yet practical deployment is still limited by two core issues: maintaining stable backgrounds and non-edited regions over time, and achieving the low latency required for real-time interactive scenarios. Meanwhile, recent streaming video generation methods are mostly developed for synthesis and cannot be directly applied to editing due to the strict preservation requirement and region-specific control. In this work, we present a novel streaming video editing framework that performs causal, frame-by-frame editing with strong content preservation and real-time responsiveness. Our key design is a three-stage distillation pipeline that progressively transfers editing capability from a powerful bidirectional foundation model to an efficient unidirectional streaming editor, enabling stable long-horizon edits without sacrificing visual fidelity. To further support real-time deployment, we introduce an AR-oriented mask cache that reuses region-related computation across frames, substantially reducing redundant processing and accelerating inference. Finally, we establish a dedicated benchmark for streaming video editing. Extensive evaluations demonstrate that our method achieves state-of-the-art visual quality among streaming baselines while drastically boosting inference speed to 12.66 FPS, making it suitable for interactive and augmented reality applications.