  We propose a novel framework for video inpainting by adopting an internal
learning strategy. Unlike previous methods that use optical flow for
cross-frame context propagation to inpaint unknown regions, we show that this
can be achieved implicitly by fitting a convolutional neural network to known
regions. Moreover, to handle challenging sequences with ambiguous backgrounds
or long-term occlusion, we design two regularization terms to preserve
high-frequency details and long-term temporal consistency. Extensive
experiments on the DAVIS dataset demonstrate that the proposed method achieves
state-of-the-art inpainting quality quantitatively and qualitatively. We
further extend the proposed method to another challenging task: learning to
remove an object from a video giving a single object mask in only one frame in
a 4K video.
