论文标题
FAIVCONF：基于AI的视频会议的面部增强，比特率低
FAIVConf: Face enhancement for AI-based Video Conference with Low Bit-rate
论文作者
论文摘要
最近，高质量的视频会议较少，传输位较少已成为一个非常炎热且充满挑战的问题。我们提出了FAIVCONF，这是一个基于有效的神经人类面部生成技术的特殊设计的视频压缩框架。 FaivConf汇集了几种设计，以改善实际视频会议场景中的系统鲁棒性：面部交换，以避免在背景动画中进行工件；面部模糊以降低传输位量并保持提取的面部地标的质量；面部视图插值的动态源更新，以适应各种头姿势。我们的方法在视频会议上实现了显着的比率降低，与H.264和H.265编码方案相比，在相同的位率下，视觉质量更好。
Recently, high-quality video conferencing with fewer transmission bits has become 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 the quality of extracted facial landmarks; and dynamic source update for face view interpolation to accommodate a large range of head poses. Our method achieves a significant bit-rate reduction in the video conference and gives much better visual quality under the same bit-rate compared with H.264 and H.265 coding schemes.
