부산대학교 도서관

Video inpainting aims to fill in missing regions of a video after any undesired contents are removed from it. This technique can be applied to repair the broken video or edit the video content. In this paper, we propose a depth-guided deep video inpainting network (DGDVI) and demonstrate its effectiveness in processing challenging broken areas crossing multiple depth layers. To achieve our goal, we divide the inpainting into depth completion, content reconstruction, and content enhancement. Three corresponding modules are designed to implement a process-flow. Firstly, we develop a depth completion module based upon the spatio-temporal Transformer which is used to obtain the completed depth information for each video frame. Secondly, we design a content reconstruction module to generate initially inpainted video. With this module, the contents of the missing regions are composed via the depth-guided feature propagation. Thirdly, we construct a content enhancement module to enhance the temporal coherence and texture quality for the inpainted video. All of proposed modules are jointly optimized to guarantee the high inpainting efficiency. The experimental results demonstrate that our proposed method provides better inpainting results, both qualitatively and quantitatively, compared with the previous state-of-the-art.