  Video inpainting aims to fill spatio-temporal "corrupted" regions with
plausible content. To achieve this goal, it is necessary to find
correspondences from neighbouring frames to faithfully hallucinate the unknown
content. Current methods achieve this goal through attention, flow-based
warping, or 3D temporal convolution. However, flow-based warping can create
artifacts when optical flow is not accurate, while temporal convolution may
suffer from spatial misalignment. We propose 'Progressive Temporal Feature
Alignment Network', which progressively enriches features extracted from the
current frame with the feature warped from neighbouring frames using optical
flow. Our approach corrects the spatial misalignment in the temporal feature
propagation stage, greatly improving visual quality and temporal consistency of
the inpainted videos. Using the proposed architecture, we achieve
state-of-the-art performance on the DAVIS and FVI datasets compared to existing
deep learning approaches. Code is available at
https://github.com/MaureenZOU/TSAM.
