Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework

Video content is widely applied in the multimedia field. Meanwhile, artificial intelligence generative content (AIGC) is continuously developing. As a result, video editing techniques in the field of computer vision are constantly evolving. However, at present, there are still numerous challenges in achieving fine-grained local editing of videos and maintaining temporal consistency.In this paper, we propose a novel framework for local video editing that integrates image-based local editing techniques with CoDeF (Content Deformation Field). Unlike existing methods, our approach explicitly models temporal deformation, ensuring spatial precision in local edits while maintaining high temporal consistency. Our framework first utilizes CoDeF to generate canonical images that aggregate static video content while preserving semantic integrity. A dual diffusion-based local editing module is then applied to modify these canonical images with user-specified editing regions and reference images. Finally, the learned temporal deformation field propagates the edits back to the entire video, achieving coherent and seamless video editing. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches in both editing quality and user satisfaction, offering a significant improvement in local precision and temporal stability.