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.
@article{arxiv.2206.14797,
title = {3D-Aware Video Generation},
author = {Sherwin Bahmani and Jeong Joon Park and Despoina Paschalidou and Hao Tang and Gordon Wetzstein and Leonidas Guibas and Luc Van Gool and Radu Timofte},
journal= {arXiv preprint arXiv:2206.14797},
year = {2023}
}