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

2026-05-14

AI Summary

Existing tuning-free video editing methods struggle to effectively leverage the rich information embedded in noisy latent spaces, resulting in limited editing quality. This work proposes a training-free, instruction-based video editing framework that introduces two key innovations: a Structured Noise Initialization Strategy (SNIS) and a Noise Guidance Mechanism (NGM). By explicitly modeling structured priors in the latent space, the approach enables precise control over edited regions while preserving global visual consistency. The method harnesses the inherent video priors of generative models to guide the denoising process, achieving state-of-the-art performance across multiple benchmarks. It significantly outperforms current tuning-free approaches in both editing quality and controllability.

Abstract

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.

Problem

• video editing
• tuning-free
• noisy latent
• instruction-based
• visual coherence

Innovation

• tuning-free
• instruction-based video editing
• Structural Noise Initialization
• Noise Guidance Mechanism
• noisy latent