Exploiting optical flow guidance for parallel structure video inpainting

Abstract

To solve the problem of video repair, we propose a new optical flow guidance solution that uses parallel structured convolution and attention networks to jointly infer video missing regions. In the network, a parallel structural model based on convolution and attention networks guided by optical flow is used to extract feature information to integrate the spatial and temporal context of video frames. This method integrates information between the target frame and the reference frame. To enhance feature learning capabilities using convolution and attention mechanisms, the feature fusion module fuses local and global features in an interactive manner, maximizing the retention of local and global representations. Our model produces visually satisfactory and time consistent results, while demonstrating on two benchmark datasets that our method outperforms the most advanced methods in terms of quantity and user research.