In this work, we investigate the problem of creating high-fidelity 3D content from only a single
image. This is inherently challenging: it essentially involves estimating the underlying 3D …

Generative adversarial networks (GANs) have drawn enormous attention due to their simple
yet effective training mechanism and superior image generation quality. With the ability to …

A recent trend in generative modeling is building 3D-aware generators from 2D image
collections. To induce the 3D bias, such models typically rely on volumetric rendering, which …

GAN inversion aims to invert a given image back into the latent space of a pretrained GAN
model so that the image can be faithfully reconstructed from the inverted code by the …

Learning a good image prior is a long-term goal for image restoration and manipulation.
While existing methods like deep image prior (DIP) capture low-level image statistics, there …

Generative adversarial networks (GANs), a novel framework for training generative models
in an adversarial setup, have attracted significant attention in recent years. The two …

Inverse graphics aims to recover 3D models from 2D observations. Utilizing differentiable
rendering, recent 3D-aware generative models have shown impressive results of rigid object …

The style-based GAN (StyleGAN) architecture achieved state-of-the-art results for generating
high-quality images, but it lacks explicit and precise control over camera poses. The recently …

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 …

Facial editing is an important task in vision and graphics with numerous applications.
However, existing works are incapable to deliver a continuous and fine-grained editing …