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 …

We address the problem of learning person-specific facial priors from a small number (eg,
20) of portrait photos of the same person. This enables us to edit this specific person's facial …

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 …

Generative models aim to learn the distribution of observed data by generating new
instances. With the advent of neural networks, deep generative models, including variational …

Various techniques have been developed and introduced to address the pressing need to
create three-dimensional (3D) content for advanced applications such as virtual reality and …

NeRFs have enabled highly realistic synthesis of human faces including complex
appearance and reflectance effects of hair and skin. These methods typically require a large …

Generating photorealistic images with controllable camera pose and scene contents is
essential for many applications including AR/VR and simulation. Despite the fact that rapid …

Abstract Generative Neural Radiance Field (GNeRF) models, which extract implicit 3D
representations from 2D images, have recently been shown to produce realistic images …

Existing face generators exhibit exceptional performance on faces in small to medium poses
(with respect to frontal faces) but struggle to produce realistic results for large poses. The …

Unsupervised learning of 3D human faces from unstructured 2D image data is an active
research area. While recent works have achieved an impressive level of photorealism, they …