3D-Aware Video Generation

• URL: http://arxiv.org/abs/2206.14797v4
• Date: Wed, 9 Aug 2023 07:34:03 GMT
• Title: 3D-Aware Video Generation
• Authors: Sherwin Bahmani, Jeong Joon Park, Despoina Paschalidou, Hao Tang, Gordon Wetzstein, Leonidas Guibas, Luc Van Gool, Radu Timofte
• Abstract summary: We explore 4D generative adversarial networks (GANs) that learn 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.
• Score: 149.5230191060692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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