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 …

Abstract We present Neural Head Avatars, a novel neural representation that explicitly
models the surface geometry and appearance of an animatable human avatar that can be …

We present dynamic neural radiance fields for modeling the appearance and dynamics of a
human face. Digitally modeling and reconstructing a talking human is a key building-block …

Abstract We present Instant Volumetric Head Avatars (INSTA), a novel approach for
reconstructing photo-realistic digital avatars instantaneously. INSTA models a dynamic …

Face reconstruction and tracking is a building block of numerous applications in AR/VR,
human-machine interaction, as well as medical applications. Most of these applications rely …

Abstract We present Face Swapping GAN (FSGAN) for face swapping and reenactment.
Unlike previous work, FSGAN is subject agnostic and can be applied to pairs of faces …

We propose an efficient framework, called Simple Swap (SimSwap), aiming for generalized
and high fidelity face swapping. In contrast to previous approaches that either lack the ability …

In this work, we propose a novel two-stage framework, called FaceShifter, for high fidelity
and occlusion aware face swapping. Unlike many existing face swapping works that …

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 …