Abstract: Video inpainting aims to fill the holes across different frames upon limited spatio-temporal contexts. The existing schemes still suffer from achieving precise spatio-temporal coherence especially in hole areas due to inaccurate modeling of motion trajectories. In this paper, we introduce fexible shape-adaptive mesh as basic processing unit and mesh flow as motion representation, which has the capability of describing complex motions in hole areas more precisely and efficiently. We propose a Mesh Oriented Video Inpainting nEtwork, dubbed MOVIE, to estimate mesh flows then complete the hole region in the video. Specifically, we first design a mesh flow estimation module and a mesh flow completion module to estimate the mesh flow for visible contents and holes in a sequential way, which decouples the mesh flow estimation for visible and corrupted contents for easy optimization. A hybrid loss function is further introduced to optimize the flow estimation performance for the visible regions, the entire frames and the inpainted regions respectively. Then we design a polishing network to correct the distortion of the inpainted results caused by mesh flow transformation. Extensive experiments show that MOVIE not only achieves over four-times speed-up in completing the missing area, but also yields more promising results with much better inpainting quality in both quantitative and perceptual metrics.

Key words: mesh flow, deep neural networks, video inpainting