mlsanityData to 14 Oct 2026

One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing

arXiv 2609.04190 ↗First posted 3 Sep 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

Video editing spans diverse editing paradigms, yet achieving high-quality instruction-guided and subject-guided editing within a single unified framework remains challenging. We introduce EditVid, a training-free framework combining sparse causal memory for local coherence, correspondence-based post-attention token injection for long-range identity preservation, and soft latent blending for edit locality. The same framework supports instruction-guided and reference-guided edits, including style transfer, attribute modification, object insertion, part-level editing, and subject replacement. On FiVE, EditVid achieves 78.16 FiVE-Acc, compared with 58.95 for the strongest evaluated training-free baseline, while obtaining competitive results on IVEBench. A user study further shows a 51.8% overall preference for EditVid over 7 competing methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 51 of its 58 references that are in the map. First posted on arXiv 3 Sep 2026.

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References · 51

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38 more
  1. 2023FateZero: Fusing Attentions for Zero-shot Text-based Video Editingcited by 466
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