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Free-form video inpainting is a very challenging task that could be widelyused for video editing such as text removal. Existing patch-based methods couldnot handle non-repetitive structures such as faces, while directly applyingimage-based inpainting models to videos will result in temporal inconsistency(see http://bit.ly/2Fu1n6b ). In this paper, we introduce a deep learn-ingbased free-form video inpainting model, with proposed 3D gated convolutions totackle the uncertainty of free-form masks and a novel Temporal PatchGAN loss toenhance temporal consistency. In addition, we collect videos and design afree-form mask generation algorithm to build the free-form video inpainting(FVI) dataset for training and evaluation of video inpainting models. Wedemonstrate the benefits of these components and experiments on both theFaceForensics and our FVI dataset suggest that our method is superior toexisting ones. Related source code, full-resolution result videos and the FVIdataset could be found on Githubhttps://github.com/amjltc295/Free-Form-Video-Inpainting .

Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGAN