Easy access to audio-visual content on social media, combined with the availability of
modern tools such as Tensorflow or Keras, and open-source trained models, along with …

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 …

Existing face forgery detectors mainly focus on specific forgery patterns like noise
characteristics, local textures, or frequency statistics for forgery detection. This causes …

Face forgery by deepfake is widely spread over the internet and has raised severe societal
concerns. Recently, how to detect such forgery contents has become a hot research topic …

While accurate lip synchronization has been achieved for arbitrary-subject audio-driven
talking face generation, the problem of how to efficiently drive the head pose remains …

Talking head video generation aims to produce a synthetic human face video that contains
the identity and pose information respectively from a given source image and a driving …

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 …

Generating expressive talking heads is essential for creating virtual humans. However,
existing one-or few-shot methods focus on lip-sync and head motion, ignoring the emotional …

One-shot talking face generation aims at synthesizing a high-quality talking face video from
an arbitrary portrait image, driven by a video or an audio segment. In this work, we provide a …

Generating portrait images by controlling the motions of existing faces is an important task of
great consequence to social media industries. For easy use and intuitive control …