3D-Aware Video Generation

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.

Authors

• Sherwin Bahmani
• Jeong Joon Park
• Despoina Paschalidou
• Hao Tang Carnegie Mellon University
• Gordon Wetzstein
• Leonidas Guibas
• Luc Van Gool INSAIT - Sofia Un.
• Radu Timofte University of Würzburg

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