无需调优的指令式视频编辑：通过结构噪声初始化和引导
Tuning-free Instruction-based Video Editing Via Structural Noise Initialization and Guidance
AI总结：
本文提出无需调优的指令式视频编辑框架，通过结构噪声初始化策略和噪声引导机制，提升视频编辑的视觉质量和性能。
摘要：
视频编辑面临重大挑战。尽管一系列无需调优的方法避免了大量数据收集和模型训练的需求，但它们往往未能充分利用嵌入在噪声潜在空间中的丰富信息，导致结果不满意。为此，我们提出一种无需调优、基于指令的视频编辑框架。我们从噪声潜在空间的角度出发：设计了结构噪声初始化策略（SNIS），通过为编辑区域分配更高的噪声水平（以促进内容变化）和为未编辑区域分配更低的噪声水平（以保持内容一致性），从而获得更优的编辑起点。我们引入了噪声引导机制（NGM），利用生成模型中的视频先验知识，有效整合噪声潜在空间中的丰富信息以引导去噪过程，从而保持未编辑内容和整体视觉一致性。实验表明，我们提出的方法在视觉质量和性能上均优于现有方法。
英文摘要：
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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