Researchers From Stanford and NVIDIA Introduce A Tri-Plane-Based 3D GAN Framework To Enable High-Resolution Geometry-Aware Image Synthesis

Generative Adversarial Networks (GANs) have been one of the main hypes of recent years. Based on the famous generator-discriminator mechanism, their very simple functioning has driven the research to continuously improve the former architecture. The peak in image generation has been reached by StyleGANs, which can produce astonishingly realistic and high-quality images, able to fool even humans.

While the generation of new samples has achieved excellent results in the 2D domain, 3D GANs are still highly inefficient. If the exact …

artificial framework gan geometry nvidia researchers stanford