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

2026-06-25 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

LiveEdit is a novel streaming video editing framework designed to overcome limitations in real-time interactive video editing, specifically maintaining stable backgrounds and achieving low latency. This system performs causal, frame-by-frame editing with strong content preservation and real-time responsiveness. Its core innovation is a three-stage distillation pipeline that progressively transfers editing capabilities from a powerful bidirectional foundation model to an efficient unidirectional streaming editor, ensuring stable long-horizon edits without visual fidelity loss. To further enhance real-time deployment, LiveEdit incorporates an AR-oriented mask cache, which reuses region-related computations across frames, significantly reducing redundant processing. Extensive evaluations show LiveEdit achieves state-of-the-art visual quality among streaming baselines while boosting inference speed to 12.66 FPS, making it suitable for interactive and augmented reality applications.

Key takeaway

For Computer Vision Engineers developing real-time streaming video editing or augmented reality applications, LiveEdit offers a solution to critical latency and stability challenges. You should consider its three-stage distillation pipeline and AR-oriented mask cache to achieve 12.66 FPS inference speeds and stable long-horizon edits. This approach allows you to deploy interactive video editing features previously limited by computational overhead and content preservation issues.

Key insights

LiveEdit enables real-time, stable streaming video editing via a three-stage distillation pipeline and AR-oriented mask cache.

Principles

• Causal, frame-by-frame editing ensures real-time responsiveness.
• Distillation transfers complex model capabilities to efficient streaming editors.
• Reusing region-related computation accelerates inference significantly.

Method

A three-stage distillation pipeline transfers editing capability from a bidirectional foundation model to a unidirectional streaming editor. An AR-oriented mask cache reuses region-related computation across frames.

In practice

• Apply to interactive video editing.
• Integrate into augmented reality apps.
• Use for stable long-horizon video edits.

Topics

• Streaming Video Editing
• Diffusion Models
• Real-Time Processing
• Augmented Reality
• Inference Acceleration
• LiveEdit Framework

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.