  Generative models have emerged as an essential building block for many image
synthesis and editing tasks. Recent advances in this field have also enabled
high-quality 3D or video content to be generated that exhibits either
multi-view or temporal consistency. With our work, we explore 4D generative
adversarial networks (GANs) that learn unconditional generation of 3D-aware
videos. By combining neural implicit representations with time-aware
discriminator, we develop a GAN framework that synthesizes 3D video supervised
only with monocular videos. We show that our method learns a rich embedding of
decomposable 3D structures and motions that enables new visual effects of
spatio-temporal renderings while producing imagery with quality comparable to
that of existing 3D or video GANs.
