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.
@article{arxiv.2002.08700,
title = {A Neural Lip-Sync Framework for Synthesizing Photorealistic Virtual News Anchors},
author = {Ruobing Zheng and Zhou Zhu and Bo Song and Changjiang Ji},
journal= {arXiv preprint arXiv:2002.08700},
year = {2021}
}