VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing

• URL: http://arxiv.org/abs/2306.08707v4
• Date: Tue, 2 Apr 2024 11:08:12 GMT
• Title: VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing
• Authors: Paul Couairon, Clément Rambour, Jean-Emmanuel Haugeard, Nicolas Thome,
• Abstract summary: We introduce VidEdit, a novel method for zero-shot text-based video editing. Our experiments show that VidEdit outperforms state-of-the-art methods on DAVIS dataset.
• Score: 18.24307442582304
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recently, diffusion-based generative models have achieved remarkable success for image generation and edition. However, existing diffusion-based video editing approaches lack the ability to offer precise control over generated content that maintains temporal consistency in long-term videos. On the other hand, atlas-based methods provide strong temporal consistency but are costly to edit a video and lack spatial control. In this work, we introduce VidEdit, a novel method for zero-shot text-based video editing that guarantees robust temporal and spatial consistency. In particular, we combine an atlas-based video representation with a pre-trained text-to-image diffusion model to provide a training-free and efficient video editing method, which by design fulfills temporal smoothness. To grant precise user control over generated content, we utilize conditional information extracted from off-the-shelf panoptic segmenters and edge detectors which guides the diffusion sampling process. This method ensures a fine spatial control on targeted regions while strictly preserving the structure of the original video. Our quantitative and qualitative experiments show that VidEdit outperforms state-of-the-art methods on DAVIS dataset, regarding semantic faithfulness, image preservation, and temporal consistency metrics. With this framework, processing a single video only takes approximately one minute, and it can generate multiple compatible edits based on a unique text prompt. Project web-page at https://videdit.github.io

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