Edit-VAR: Taming Visual Autoregressive Model for Precise Video Editing

2026-09-17

AI Summary

本文提出Edit-VAR，一种无需训练和反演的文本引导视频编辑框架，通过预训练视觉自回归模型实现精准编辑，同时保持源内容和时间一致性。

Abstract

Text-guided video editing modifies target content while preserving the appearance and temporal coherence of unedited regions. Training-based approaches provide strong control but demand substantial data and computation. Training-free methods fall into inversion-free and inversion-based paradigms. Inversion-free approaches avoid trajectory recovery, but their source-preserving guidance can limit editing strength and leave semantic changes incomplete. Inversion-based approaches recover a latent trajectory before regeneration, where approximation errors can accumulate and cause source-content drift and temporal inconsistency. We introduce Edit-VAR, the first training-free and inversion-free framework for text-guided video editing with a pretrained visual autoregressive video model. Edit-VAR directly encodes the source video into multi-scale discrete tokens and performs probability-guided conditional token replacement for source preservation. Attention-guided token-wise and scale-aware modulation selectively relaxes source constraints over edit-relevant positions and generation stages. Scale-Decoupled Generation, implemented as late-scale constraint release, regenerates motion-consistent details and reduces texture fragmentation. Residual-guided token pruning further exploits redundancy at the final two high-resolution scales to reduce inference cost. Extensive experiments and a blind user study demonstrate that Edit-VAR outperforms existing training-free video editing methods overall in editing fidelity, source preservation, temporal coherence, and inference efficiency.

Problem

• text-guided video editing
• temporal coherence
• inversion-free
• inversion-based
• source preservation

Innovation

• training-free
• inversion-free
• probability-guided conditional token replacement
• scale-aware modulation
• scale-decoupled generation