SOURCE-LINKED INTELLIGENCE
One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing
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
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-03T17:59:01.000Z
First collected: 2026-09-21T04:31:57.454Z. This is not the publication date.