Abstract 3D-aware generative adversarial networks (GANs) synthesize high-fidelity and
multi-view-consistent facial images using only collections of single-view 2D imagery …

Existing 3D-aware facial generation methods face a dilemma in quality versus editability:
they either generate editable results in low resolution, or high-quality ones with no editing …

We present AvatarReX, a new method for learning NeRF-based full-body avatars from video
data. The learnt avatar not only provides expressive control of the body, hands and the face …

Deepfake is a technology dedicated to creating highly realistic facial images and videos
under specific conditions, which has significant application potential in fields such as …

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 …

This review thoroughly examines the role of semantically-aware Neural Radiance Fields
(NeRFs) in visual scene understanding, covering an analysis of over 250 scholarly papers. It …

Existing approaches to animatable NeRF-based head avatars are either built upon face
templates or use the expression coefficients of templates as the driving signal. Despite the …

We present a method that reconstructs and animates a 3D head avatar from a single-view
portrait image. Existing methods either involve time-consuming optimization for a specific …

Neural rendering combines ideas from classical computer graphics and machine learning to
synthesize images from real-world observations. NeRF, short for Neural Radiance Fields, is …

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 …