Oct 2026· Multimedia Systems· Vol 32· 0 citations· 37 references
TL;DR
A Vision-Language Integrated Video Editing (VLIVE) framework in which these operations are performed within a sequential modular pipeline, showing that the proposed framework maintains favorable semantic alignment, temporal consistency, and visual quality across the evaluated short-video datasets.
VINCIE-NExT is introduced, a unified framework that transfers editing capability from images to videos through in-context visual demonstrations, alleviating the need for large-scale paired video editing data and provides principled test-time scaling.
Lei-Gang Qu, Feng Cheng, Zi-Yan Yang et al.· 0 citations
OmniXRef, a unified framework for multimodal multi-reference video editing that allows users to combine variable numbers of text, image, and video references to control diverse attributes of a source video, is introduced.
Xiao-Yan Cong, Tian-Yu Wang, Hyeonho Jeong et al.· 0 citations
This paper proposes SVEET, a framework that requires merely training on a pretrained bidirectional video diffusion model but supports high-quality streaming video editing in an auto-regressive fashion and proposes a decoupled training scheme that explicitly enforces the orthogonality between the optimization directions...
Yu-Jia Hu, Jia-Jun Li, Zi-Hao He et al.· 0 citations
While recent video foundation models excel at generating high-quality short videos, long-form video generation remains a critical challenge, where a major bottleneck lies in conditioning independently generated shots to preserve consistent characters, scenes, and objects throughout a story. Existing training-free appro...
Xiang-Han Wei, Xiao-Da Yang, Zhi Wang et al.· 0 citations
Text-guided video editing modifies target content while preserving the appearance and temporal coherence of unedited regions. Training-based approaches provide strong control but demand substantial data and computation. Training-free methods fall into inversion-free and inversion-based paradigms. Inversion-free approac...
Chong-Bo Zhao, Jiangming Wang, Xilai Wang et al.· 0 citations
Recent image editing systems have achieved impressive semantic understanding, visual fidelity, and instruction-following ability, while video editing remains substantially more difficult and costly. In this paper, we present a simple alternative to end-to-end video editing: instead of training a monolithic video editor...
Feng Wang, Zi-Jie Li, Ce-Yuan Yang et al.· 0 citations
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