Generative deep learning algorithms have progressed to a point where it is difficult to tell the
difference between what is real and what is fake. In 2018, it was discovered how easy it is to …

The computer graphics and vision communities have dedicated long standing efforts in
building computerized tools for reconstructing, tracking, and analyzing human faces based …

Traditional 3D morphable face models (3DMMs) provide fine-grained control over
expression but cannot easily capture geometric and appearance details. Neural volumetric …

We propose a neural talking-head video synthesis model and demonstrate its application to
video conferencing. Our model learns to synthesize a talking-head video using a source …

We present a novel semantic model for human head defined with neural radiance field. The
3D-consistent head model consist of a set of disentangled and interpretable bases, and can …

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 …

To facilitate the analysis of human actions, interactions and emotions, we compute a 3D
model of human body pose, hand pose, and facial expression from a single monocular …

Recently, deep learning based 3D face reconstruction methods have shown promising
results in both quality and efficiency. However, training deep neural networks typically …

Learned 3D representations of human faces are useful for computer vision problems such
as 3D face tracking and reconstruction from images, as well as graphics applications such …

We present a novel approach that enables photo-realistic re-animation of portrait videos
using only an input video. In contrast to existing approaches that are restricted to …