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We present the first neural video compression method based on generativeadversarial networks (GANs). Our approach significantly outperforms previousneural and non-neural video compression methods in a user study, setting a newstate-of-the-art in visual quality for neural methods. We show that the GANloss is crucial to obtain this high visual quality. Two components make the GANloss effective: we i) synthesize detail by conditioning the generator on alatent extracted from the warped previous reconstruction to then ii) propagatethis detail with high-quality flow. We find that user studies are required tocompare methods, i.e., none of our quantitative metrics were able to predictall studies. We present the network design choices in detail, and ablate themwith user studies.

Neural Video Compression using GANs for Detail Synthesis and Propagation