  When trying to independently apply image-trained algorithms to successive
frames in videos, noxious flickering tends to appear. State-of-the-art
post-processing techniques that aim at fostering temporal consistency, generate
other temporal artifacts and visually alter the style of videos. We propose a
postprocessing model, agnostic to the transformation applied to videos (e.g.
style transfer, image manipulation using GANs, etc.), in the form of a
recurrent neural network. Our model is trained using a Ping Pong procedure and
its corresponding loss, recently introduced for GAN video generation, as well
as a novel style preserving perceptual loss. The former improves long-term
temporal consistency learning, while the latter fosters style preservation. We
evaluate our model on the DAVIS and videvo.net datasets and show that our
approach offers state-of-the-art results concerning flicker removal, and better
keeps the overall style of the videos than previous approaches.
