# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 1 Introduction  
In today's era of booming multimedia content creation, video editing has become a core element in shaping engaging visual experiences. However, the existing video-editing technologies are gradually revealing multiple limitations when faced with the ever-growing user demands and complex editing tasks. Traditional video editing means, such as manual cutting, splicing and processing based on simple filters [1, 2, 3], are rather inadequate when it comes to handling complex video object-level editing and precise local modifications of videos. These methods, which often rely on cumbersome manual operations [4, 5], are not only inefficient but also struggle to ensure the accuracy and consistency of the editing effects. Moreover, for example, when attempting to modify the detailed features of specific objects in a video (such as changing the texture, shape or color of an object), traditional methods require a huge amount of time for frame-by-frame processing, and are prone to creating obvious visual differences between adjacent frames, which will disrupt the coherence of the video [6, 7]. Even some advanced traditional video editing software is liable to cause problems like blurry edgesand unnatural color transitions when dealing with complex local editing requirements, seriously affecting the visual quality of the edited video. With the extensive application of deep learning technology in the field of computer vision, the video editing field has also embraced new development opportunities. Specifically, [PERSON] et al. [8] put forward a method for transferring the image style to video sequences. Based on the static image style transfer technology, it solves problems such as flickering and discontinuity in videos by introducing new initialization and loss functions. [PERSON] et al. [9] came up with a CNN-based video style transfer approach. It drew on the principle of image style transfer, applied artistic styles to each frame of the video, and generated videos that conformed to the target style by extracting content and style features. However, these processing methods based on neural networks not only have high computational complexity and large resource consumption but also are slow in speed when dealing with long videos or big data, and show poor adaptability to complex dynamic scenes. [PERSON] et al. [10] presented the GANgealing algorithm, which is used to solve the problem of dense visual alignment. Users only need to label or edit on a single image, and then the editing effect can be easily applied to videos of the same category through forward propagation. However, this method relies on the Gan model [11], which may lead to deviations in the learned correspondences. Similarly, [PERSON] et al. [12] proposed a sketch-based face video editing framework, which can apply sketch editing to key frames and then propagate the sketch-editing effect to the entire video. [PERSON] et al. [13] make use of the characteristics of StyleGAN [14] and the learning of motion tendencies by neural networks to solve the problem of temporal consistency in video face editing to some extent through the coordinated work of six components such as temporally consistent alignment and encoder inversion. However, the two methods mentioned above are both limited by StyleGAN and have problems such as difficulty in generating personalized results, poor local-region processing, and artifact generation. [PERSON] et al. [15] put forward an innovative framework--LNA (Layered Neural Atlases). This method realizes the control of the global scene during the video editing process by decompose-ing video frames into multiple layers and using neural networks to model each layer. Inspired by the research on LNA, [PERSON] et al. [16] proposed an Inter-active Neural Network Video Editing framework (INVE), which allows users to easily specify editing areas (such as object replacement or modification) in videos. However, both of these two methods have relatively high computational costs and weak gener-alization abili-ties.