WAIT: Feature Warping for Animation to Illustration video Translation
  using GANs
Samet Hicsonmez Nermin Samet Fidan Samet Oguz Bakir Emre Akbas Pinar Duygulu
In this paper, we explore a new domain for video-to-video translation.
Motivated by the availability of animation movies that are adopted from
illustrated books for children, we aim to stylize these videos with the style
of the original illustrations. Current state-of-the-art video-to-video
translation models rely on having a video sequence or a single style image to
stylize an input video. We introduce a new problem for video stylizing where an
unordered set of images are used. This is a challenging task for two reasons:
i) we do not have the advantage of temporal consistency as in video sequences;
ii) it is more difficult to obtain consistent styles for video frames from a
set of unordered images compared to using a single image.
  Most of the video-to-video translation methods are built on an image-to-image
translation model, and integrate additional networks such as optical flow, or
temporal predictors to capture temporal relations. These additional networks
make the model training and inference complicated and slow down the process. To
ensure temporal coherency in video-to-video style transfer, we propose a new
generator network with feature warping layers which overcomes the limitations
of the previous methods. We show the effectiveness of our method on three
datasets both qualitatively and quantitatively. Code and pretrained models are
available at https://github.com/giddyyupp/wait.