  We present the first neural video compression method based on generative
adversarial networks (GANs). Our approach significantly outperforms previous
neural and non-neural video compression methods in a user study, setting a new
state-of-the-art in visual quality for neural methods. We show that the GAN
loss is crucial to obtain this high visual quality. Two components make the GAN
loss effective: we i) synthesize detail by conditioning the generator on a
latent extracted from the warped previous reconstruction to then ii) propagate
this detail with high-quality flow. We find that user studies are required to
compare methods, i.e., none of our quantitative metrics were able to predict
all studies. We present the network design choices in detail, and ablate them
with user studies.

Image and Video Processing;Computer Vision and Pattern Recognition