We present a high-fidelity 3D generative adversarial network (GAN) inversion framework
that can synthesize photo-realistic novel views while preserving specific details of the input …

Abstract 3D GAN inversion aims to achieve high reconstruction fidelity and reasonable 3D
geometry simultaneously from a single image input. However, existing 3D GAN inversion …

Recently, a surge of high-quality 3D-aware GANs have been proposed, which leverage the
generative power of neural rendering. It is natural to associate 3D GANs with GAN inversion …

The increased demand for 3D data in AR/VR robotics and gaming applications gave rise to
powerful generative pipelines capable of synthesizing high-quality 3D objects. Most of these …

With the recent advances in NeRF-based 3D aware GANs quality, projecting an image into
the latent space of these 3D-aware GANs has a natural advantage over 2D GAN inversion …

Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using
only collections of single-view 2D photographs has been a long-standing challenge …

While 2D generative adversarial networks have enabled high-resolution image synthesis,
they largely lack an understanding of the 3D world and the image formation process. Thus …

Making generative models 3D-aware bridges the 2D image space and the 3D physical
world yet remains challenging. Recent attempts equip a Generative Adversarial Network …

We present a one-shot method to infer and render a photorealistic 3D representation from a
single unposed image (eg, face portrait) in real-time. Given a single RGB input, our image …

We introduce a high resolution, 3D-consistent image and shape generation technique which
we call StyleSDF. Our method is trained on single view RGB data only, and stands on the …