RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models

• URL: http://arxiv.org/abs/2312.04524v1
• Date: Thu, 7 Dec 2023 18:43:45 GMT
• Title: RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models
• Authors: Ozgur Kara, Bariscan Kurtkaya, Hidir Yesiltepe, James M. Rehg, Pinar Yanardag
• Abstract summary: RAVE is a zero-shot video editing method that leverages pre-trained text-to-image diffusion models without additional training. It produces high-quality videos while preserving original motion and semantic structure. RAVE is capable of a wide range of edits, from local attribute modifications to shape transformations.
• Score: 19.792535444735957
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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