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MDN-Control is a training‑free framework for multi‑subject video editing that jointly controls target localization, occlusion geometry, and appearance initialization. It uses mask‑guided localization for consistent target placement, depth‑aware occlusion control to resolve boundaries between overlapping subjects, and noise‑latent prompting to retrieve Gaussian initializations from a noise library for prompt‑relevant priors. Experiments on MSVBench show that MDN‑Control achieves the lowest CM‑Err and the highest Q‑Edit while maintaining competitive text alignment and temporal consistency.
Text-conditioned image-to-video (I2V) generation has advanced rapidly, yet generating videos with multiple subjects remains challenging. A model must simultaneously preserve the appearance of each sub...
arXiv:2609.36503v1 Announce Type: new Abstract: Adding or removing a sounding object requires coordinated changes to visual content and sound while preserving the surrounding scene. Yet paired superv...
arXiv:2608.22819v1 Announce Type: new Abstract: Text-conditioned image-to-video (I2V) generation has advanced rapidly, yet generating videos with multiple subjects remains challenging. A model must s...
FacEDiT is a mask‑controlled model for editing local segments of talking head videos by infilling facial motion from speech and surrounding context. It uses a self‑supervised pretext task that masks a motion span during training, enabling variable‑duration local rewriting while preserving identity, unedited regions, and boundary continuity. The authors also release FacEDiTBench, a benchmark covering diverse edit types and lengths, and demonstrate that FacEDiT achieves accurate, speech‑aligned edits with strong identity preservation and seamless transitions.
The paper introduces EditVid, a training‑free framework that unifies instruction‑guided and reference‑guided video editing. It combines sparse causal memory for local coherence, correspondence‑based post‑attention token injection for long‑range identity preservation, and soft latent blending for edit locality. EditVid supports a wide range of editing tasks—including style transfer, attribute modification, object insertion, part‑level editing, and subject replacement—and outperforms the strongest training‑free baseline on FiVE while achieving competitive results on IVEBench, with a user study showing a 51.8% overall preference over seven competing methods.