SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

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 dri

Read original source ↗ Open in workspace

recordType

paper

region

Global