AI RESEARCH
MiVE: Multiscale Vision-language features for reference-guided video Editing
arXiv CS.CV
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ArXi:2605.14664v1 Announce Type: new Reference-guided video editing takes a source video, a text instruction, and a reference image as inputs, requiring the model to faithfully apply the instructed edits while preserving original motion and unedited content. Existing methods fall into two paradigms, each with inherent limitations: decoupled encoders suffer from modality gaps when processing instructions and visual content independently, while unified vision-language encoders lose fine-grained spatial details by relying solely on final-layer representations.