# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 1 Introduction
Especially in scenes with complex motion changes, there may be certain distortion or unnatural effects. It can be seen that although deep learning models have achieved certain progress in the video editing direction, there are still significant deficiencies when dealing with local areas. Against this background, drawing on some image editing methods based on diffusion models [17; 18; 19] and attempting to apply them to video editing has pro-vided a brand-new idea for video editing. Specifically, while recent diffusion-based video editing frameworks have shown some progress in video editing, they still face challenges in achieving precise localized edits with fine-grained spatial consistency. For example, CVPR 2024's CCEdit [20] highlights that existing methods often lack explicit mechanisms for aligning local semantic features across frames, leading to inconsistencies when modifying small objects or textures. This limitation is further underscored by SIGGRAPH Asia 2024's I2 VEdit [21], which demonstrates that even state-of-the-art models struggle to propagate localized image edits to videos without introducing motion artifacts or texture mismatches. [PERSON] et al. [22] put forward a new editing form called imitative editing. Users only need to simply mark the area they want to edit in the source image and provide a reference image containing the desired editing effect. Then the editing system can automatically identify the relevant content and accurately apply it to the editing area of the source image. This method is especially suitable for handling modifications of local details and fine editing of complex objects. Despite the effectiveness of this imitative editing method in image processing, directly applying image editing methods to video editing presents numerous challenges. In this paper, we propose an innovative video editing method. This method combines the local image editing method with CoDeF (Content Deformation Field) [23] to achieve more precise, more meticulous editing operations on the local content of videos while maintaining temporal consistency. Specifically, we first produce the canonical image by utilizing the content deformation field. During this process, by deeply analyzing and modeling the video content, the static content in the video is aggregated into the canonical image, while the temporal deformation information from the canonical image to each video frame is accurately recorded. Subsequently, after generating the canonical image, we apply the local editing module to edit it. By taking advantage of its powerful local editing capabilities, specific local areas in the canonical image are finely modified according to the editing areas specified by users and the reference images. This editing method fully exploits the advantages of analog editing in dealing with local details and can achieve precise editing of tiny objects and complex areas in the canonical image, such as modifying the local texture of an object or adding local components or other subtle features. Ultimately, we use the learned temporal deformation field to propagate the effect of the edited canonical image to the entire video. The main contributions of this research are reflected in the following aspects. Firstly, a brand-new video editing framework that combines the image editing method based on the diffusion model with CoDeF is put forward, providing an innovative solution for the fine local editing of videos. Moreover, through the meticulously designed model structure and training strategies, the performance of video editing in handling fine local tasks is remarkably enhanced. It can precisely edit tiny objects and detailed areas in videos while maintaining consistency with the surrounding environment.