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.
By Jiayi Yu, Xi Ye, Lina Wang, Yunkun Xia
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...
By Weihan Xu, Kan Jen Cheng, Koichi Saito, Jingyu Shi, Tingle Li, Yisi Liu, Liming Wang, Masato Ishii, Takashi Shibuya, Gopala Anumanchipalli, Paul Pu Liang
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...
By Yanliang Qi, Kexi Chen, Muchao Ye, Haomiao Ni
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.
By Kim Sung-Bin, Joohyun Chang, David Harwath, Tae-Hyun Oh
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.
arXiv:2607. 19895v1 Announce Type: cross Abstract: Text-guided video editing with diffusion models is impractically slow, hindered by costly multi-step sampling and inversion.
By Habin Lim, Gyeong-Moon Park
AV-FlowEdit is a training‑free, inversion‑free framework that enables prompt‑driven audio‑visual editing using joint audio‑video diffusion models. It leverages source‑to‑target flow differences to guide edits while preserving non‑target structure and cross‑modal correspondence. The method includes a Cross‑Modal Event‑Aware Spectral Trajectory Preservation module that suppresses drift by combining flow saliency, aligned audio activity, multiscale low‑frequency displacement, and diffusion‑stage scheduling, achieving strong prompt alignment, temporal consistency, and competitive audio quality.
By Shaohua Pan, Xiao Liu
The paper introduces EditVid, a training‑free framework for video editing that unifies instruction‑guided and reference‑guided edits. It employs 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.
By Adheesh Sunil Juvekar, Onkar Kishor Susladkar, Kiet A. Nguyen, Muntasir Wahed, Nabeel Bashir, Xiaona Zhou, Tianjiao Yu, Vedant Shah, Ismini Lourentzou
arXiv:2607.18227v2 Announce Type: replace
Abstract: In line with the prevailing direction of vision research, we explore the integration of both generation and editing capabilities for video and imag...
By Dingyun Zhang, Lixue Gong, Wei Liu
arXiv:2608. 06929v1 Announce Type: cross Abstract: Regional image editing has attracted considerable attention for its spatial controllability.
By Rui Xu, Yang Yong, Shunzi Yang, Ruihao Gong, Chengtao Lv
Edit‑VAR is a training‑free, inversion‑free framework that uses a pretrained visual autoregressive video model for text‑guided video editing. It encodes the source video into multi‑scale discrete tokens and applies probability‑guided conditional token replacement, attention‑guided token‑wise and scale‑aware modulation, and scale‑decoupled generation to preserve source appearance while enabling precise edits. The method also includes residual‑guided token pruning to reduce inference cost, and experimental results show it outperforms existing training‑free video editing methods in fidelity, source preservation, temporal coherence, and efficiency.
By Chongbo Zhao, Jiangming Wang, Xilai Wang, Xinyu Wang, Jingyi Tang, Chunjie Hao, Pengjie Song, Yue Ma