  We propose in this paper a new paradigm for facial video compression. We
leverage the generative capacity of GANs such as StyleGAN to represent and
compress a video, including intra and inter compression. Each frame is inverted
in the latent space of StyleGAN, from which the optimal compression is learned.
To do so, a diffeomorphic latent representation is learned using a normalizing
flows model, where an entropy model can be optimized for image coding. In
addition, we propose a new perceptual loss that is more efficient than other
counterparts. Finally, an entropy model for video inter coding with residual is
also learned in the previously constructed latent representation. Our method
(SGANC) is simple, faster to train, and achieves better results for image and
video coding compared to state-of-the-art codecs such as VTM, AV1, and recent
deep learning techniques. In particular, it drastically minimizes perceptual
distortion at low bit rates.
