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 FiVE, EditVid achieves 78.16 FiVE-Acc, compared with 58.95 for the strongest evaluated training-free baseline, while obtaining competitive results on IVEBench. A user study further shows a 51.8\% overall preference for EditVid over 7 competing methods.
The quality and diversity of instruction-based video editing datasets are steadily improving, yet existing datasets mainly focus on single editing operations and fall short in supporting compositional instruction-guided video editing. In particular, multiple editing intents must be jointly understood and faithfully exe...
Fuchen Long, Cong Wang, Zitao Gao et al.· 1 citation
The OmniEdit-Bench provides a comprehensive and reliable testbed for evaluating instruction-based video editing and offers insights into future research directions, including an accuracy-aware penalty mechanism that conditions other scores on accuracy, preventing visually plausible but incorrect edits from receiving in...
Chenxuan Miao, Yutong Feng, Yi Lu et al.· 1 citation
VideoX-Qwen, an integrated data-construction and model-training framework for general instruction-based video editing, and a unified Qwen-Wan editor that combines multimodal semantic conditioning with dense source-video latent guidance provide a practical foundation for more capable instruction-driven video editing.
J.Jenny Li, Di Shao, Xin-Yu Chen et al.· 0 citations
Interactive video generation and editing are becoming increasingly important for creative design. In this report, we introduce EditStream: a unified framework for interactive video generation and editing. EditStream unifies multiple video creation and manipulation tasks within a single DiT-based model through flexible...
Yu-Qian Zhou, Zhenghong Zhou, Zongze Wu et al.· 1 citation
GRNEdit, a lightweight two-stage framework for instruction-based general video editing that outperforms multiple 14B open-source editors, while the 8B model performs on par with leading open-source editors.
Video and audio are perceived together, yet most generative models treat them in isolation. We examine methods that model the two modalities jointly, generate one from the other, or edit them in a coupled manner, organized around a single question: how is the output kept coherent across modalities in time and semantics...
Abhinav Sharma, S. Navuluru, Wang Wei et al.· 0 citations
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
