  Lip sync has emerged as a promising technique for generating mouth movements
from audio signals. However, synthesizing a high-resolution and photorealistic
virtual news anchor is still challenging. Lack of natural appearance, visual
consistency, and processing efficiency are the main problems with existing
methods. This paper presents a novel lip-sync framework specially designed for
producing high-fidelity virtual news anchors. A pair of Temporal Convolutional
Networks are used to learn the cross-modal sequential mapping from audio
signals to mouth movements, followed by a neural rendering network that
translates the synthetic facial map into a high-resolution and photorealistic
appearance. This fully trainable framework provides end-to-end processing that
outperforms traditional graphics-based methods in many low-delay applications.
Experiments also show the framework has advantages over modern neural-based
methods in both visual appearance and efficiency.
