LiveEdit: Towards Real-Time Diffusion-Based Streaming Video Editing

Streaming video editing has made rapid progress, yet practi-cal deployment is still limited by two core issues: maintaining stable back-grounds and non-edited regions over time, and achieving the low latencyrequired for real-time interactive scenarios. Meanwhile, recent streamingvideo generation methods are mostly developed for synthesis and cannotbe directly applied to editing due to the strict preservation requirementand region-specific control. In this work, we present a novel streamingvideo editing framework that performs causal, frame-by-frame editingwith strong content preservation and real-time responsiveness. Our keydesign is a three-stage distillation pipeline that progressively transfersediting capability from a powerful bidirectional foundation model to anefficient unidirectional streaming editor, enabling stable long-horizon ed-its without sacrificing visual fidelity. To further support real-time deploy-ment, we introduce an AR-oriented mask cache that reuses region-relatedcomputation across frames, substantially reducing redundant processingand accelerating inference. Finally, we establish a dedicated benchmarkfor streaming video editing. Extensive evaluations demonstrate that ourmethod achieves state-of-the-art visual quality among streaming base-lines while drastically boosting inference speed to 12.66 FPS, making itsuitable for interactive and augmented reality applications.