We consider the problem of filling in missing spatio-temporal regions of a video. We provide a novel flow-based solution by introducing a generative model of images in relation to the scene (without missing regions) and mappings from the scene to images. We use the model to jointly infer the scene template, a 2D representation of the scene, and the mappings. This ensures consistency of the frame-to-frame flows generated to the underlying scene, reducing geometric distortions in flow based inpainting. The template is mapped to the missing regions in the video by a new L2-L1 interpolation scheme, creating crisp inpaintings and reducing common blur and distortion artifacts. We show on two benchmark datasets that our approach out-performs state-of-the-art quantitatively and in user studies.
@article{arxiv.2108.12845,
title = {Flow-Guided Video Inpainting with Scene Templates},
author = {Dong Lao and Peihao Zhu and Peter Wonka and Ganesh Sundaramoorthi},
journal= {arXiv preprint arXiv:2108.12845},
year = {2021}
}