RAVE：基于扩散模型的随机噪声混洗以实现快速且一致的视频编辑
RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models
AI总结：
本文提出零样本视频编辑方法 RAVE，通过随机噪声混洗利用帧间时空交互，在无需训练的情况下实现快速、省内存且时间一致的高质量视频编辑。
摘要：
近期，基于扩散的模型在根据文本生成图像方面取得了显著成功。然而，视频编辑模型尚未达到同等水平的视觉质量和用户可控性。为解决这一问题，我们提出 RAVE，这是一种零样本视频编辑方法，它利用预训练的文本到图像扩散模型，无需额外训练。RAVE 接收输入视频和文本提示，以生成高质量视频，同时保留原始运动和语义结构。该方法采用一种新颖的噪声混洗策略，利用帧之间的时空交互，以比现有方法更快的速度生成时间一致的视频。它在内存需求方面也很高效，因此能够处理更长的视频。RAVE 可实现范围广泛的编辑，从局部属性修改到形状变换。为了证明 RAVE 的多功能性，我们创建了一个全面的视频评估数据集，场景涵盖以物体为中心的场景、跳舞和打字等复杂人类活动，以及包含游鱼和船只的动态场景。我们的定性和定量实验突显了 RAVE 与现有方法相比在多种视频编辑场景中的有效性。我们的代码、数据集和视频可在 https://rave-video.github.io 获取。
英文摘要：
Recent advancements in diffusion-based models have demonstrated significant success in generating images from text. However, video editing models have not yet reached the same level of visual quality and user control. To address this, we introduce RAVE, a zero-shot video editing method that leverages pre-trained text-to-image diffusion models without additional training. RAVE takes an input video and a text prompt to produce high-quality videos while preserving the original motion and semantic structure. It employs a novel noise shuffling strategy, leveraging spatio-temporal interactions between frames, to produce temporally consistent videos faster than existing methods. It is also efficient in terms of memory requirements, allowing it to handle longer videos. RAVE is capable of a wide range of edits, from local attribute modifications to shape transformations. In order to demonstrate the versatility of RAVE, we create a comprehensive video evaluation dataset ranging from object-focused scenes to complex human activities like dancing and typing, and dynamic scenes featuring swimming fish and boats. Our qualitative and quantitative experiments highlight the effectiveness of RAVE in diverse video editing scenarios compared to existing methods. Our code, dataset and videos can be found in https://rave-video.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
