Instruction-Based Video Editing

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Tuning-free Instruction-based Video Editing Via Structural Noise Initialization and Guidance
May 15, 2026Song Wu, Xinyu Chen, Qian Wang +3Video Diffusion Models Diffusion Guidance
JIUTIAN Research, China · School of Intelligence Science and Technology, Nanjing University · State Key Laboratory of Novel Software Technology, Nanjing University
MiVE: Multiscale Vision-language features for reference-guided video Editing
May 14, 2026Tong Wang, Meng Zou, Chengjing Wu +4Video Diffusion Models Video Editing
MT Lab, Meitu Inc., Beijing 100083, China · Beijing University of Posts and Telecommunications, Beijing 100876, China · Department of Computer Science and Technology, BNRist, IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
OphEdit: Training-Free Text-Guided Editing of Ophthalmic Surgical Videos
May 8, 2026Ritul Jangir, Arkya Jyoti Bagchi, Aiman Farooq +3Video Diffusion Models Video Editing
Indian Institute of Technology Jodhpur · All India Institute of Medical Sciences Delhi
Sparkle: Realizing Lively Instruction-Guided Video Background Replacement via Decoupled Guidance
May 7, 2026Ziyun Zeng, Yiqi Lin, Guoqiang Liang +1Synthetic Data Augmentation Video Editing
Show Lab, National University of Singapore
LIVE: Leveraging Image Manipulation Priors for Instruction-based Video Editing
Apr 18, 2026Weicheng Wang, Zhicheng Zhang, Zhongqi Zhang +6Video Editing Benchmarks Video Diffusion Models
Nankai University · Kuaishou Technology · Pengcheng Laboratory +1
VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects
Apr 17, 2026Xiangbo Gao, Sicong Jiang, Bangya Liu +12Video Editing Benchmarks Video Editing
1Texas A&M University · 2Visko Platform · 3Abaka AI
UniVideo: Unified Understanding, Generation, and Editing for Videos
Oct 9, 2025Cong Wei, Quande Liu, Zixuan Ye +5Image-to-Video Generation Unified Multimodal Models
University of Waterloo · Kling Team, Kuaishou Technology

May 15, 2026, cs.CVJ/K move · Enter open · S save

Tuning-free Instruction-based Video Editing Via Structural Noise Initialization and Guidance

Song Wu, Xinyu Chen, Qian Wang, Liang Li+2

JIUTIAN Research, China · School of Intelligence Science and Technology, Nanjing University · State Key Laboratory of Novel Software Technology, Nanjing University

Video Diffusion Models Diffusion Guidance Video Editing Instruction-Based Video Editing

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Video editing poses a significant challenge. While a series of tuning-free methods circumvent the need for extensive data collection and model training, they often underutilize the rich information embedded within noisy latent, leading to unsatisfactory results. To address this, we propose a \textit{tuning-free, instruction-based} video editing framework. We approach video editing from the perspective of noisy latent: we design a Structural Noise Initialization Strategy (SNIS) to secure a superior editing starting point by assigning higher noise levels to edited regions (to facilitate content change) and lower noise levels to unedited regions (to maintain content consistency). We introduce a Noise Guidance Mechanism (NGM), which leverages the video prior in the generative model and effectively integrates rich information within the noisy latent to guide the denoising process, thereby preserving unedited content and overall visual coherence. Experiments show that our proposed method achieves better visual quality and state-of-the-art performance.

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