EditaLive! Unified Character Video Editing for Live Streaming

2026-08-27

AI Summary

为解决实时直播中人物视频编辑问题，提出EditaLive框架，通过改进预训练模型和设计自卷积蒸馏策略实现低延迟实时编辑。

Abstract

Conventional video editing primarily focuses on scene-level content, whereas live streaming places greater emphasis on the human subject. However, directly applying existing video-editing methods to human-centric live streaming remains challenging, as they may introduce facial-expression inconsistencies and typically depend on multiple offline inference steps, making them unsuitable for real-time interaction. We propose EditaLive, a novel framework for real-time streaming character video editing. In detail, we start from a pretrained image animation model (Wan-Animate), which naturally decouples appearance from motion, and repurpose it as the base model for instruction-based human-centric video editing by reference frame editing and video reconstruction via the collected CharEdit-50K dataset. Besides, we adapt the model from offline bidirectional to causal streaming generation, and design an aligned self-rollout distillation strategy that compresses the model into a two-step sampler, where fixed RoPE and align forcing reduce training--inference discrepancies, and first-frame preserved sparse attention filters redundant historical information to mitigate appearance drift. Extensive experiments demonstrate that EditaLive delivers state-of-the-art editing performance with faithful preservation of facial expressions and low-latency real-time streaming inference.

Problem

• live streaming
• video editing
• facial-expression inconsistencies
• real-time interaction

Innovation

• real-time streaming
• human-centric video editing
• causal streaming generation
• aligned self-rollout distillation
• sparse attention