FAIVconf: Face Enhancement for AI-Based Video Conference with Low Bit-Rate

Z. Li, Sheng-fu Lin, Shan Liu, Songnan Li, Xue Lin, Wei Wang, Wei Jiang
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引用次数: 1

Abstract

Recently, high-quality video conferencing with fewer transmission bits becomes a very hot and challenging problem. We propose FAIVConf, a specially designed video compression framework for video conferencing, based on the effective neural human face generation techniques. FAIVConf brings together several designs to improve the system robustness in real video conference scenarios: face swapping to avoid artifacts in background animation; facial blurring to decrease transmission bit-rate and maintain quality of extracted facial landmarks; and dynamic source update for face view interpolation to accommodate a large range of head poses. Our method achieves significant bit-rate reduction in video conference and gives much better visual quality under the same bit-rate compared with H.264 and H.265 coding schemes.
FAIVconf:基于人工智能的低比特率视频会议的人脸增强
近年来,少传输位的高质量视频会议成为一个非常热门和具有挑战性的问题。基于有效的神经人脸生成技术,提出了专为视频会议设计的视频压缩框架FAIVConf。FAIVConf汇集了几种设计来提高系统在真实视频会议场景中的鲁棒性:人脸交换以避免背景动画中的伪像;人脸模糊,以降低传输比特率和保持提取的人脸特征的质量;动态源更新的面部视图插值,以适应大范围的头部姿态。与H.264和H.265编码方案相比,我们的方法在视频会议中实现了显着的比特率降低,并且在相同比特率下提供了更好的视觉质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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