3D-Aware Video Generation
Sherwin Bahmani, Jeong Joon Park, Despoina Paschalidou, Hao Tang, Gordon Wetzstein, Leonidas Guibas, Luc Van Gool, Radu Timofte
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.