Generative Face Video Coding Techniques and Standardization Efforts: A Review

被引:2
|
作者
Chen, Bolin [1 ]
Chen, Jie [2 ]
Wang, Shiqi [1 ]
Ye, Yan [2 ]
机构
[1] City Univ Hong Kong, Hong Kong, Peoples R China
[2] Alibaba Grp, Hangzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
EFFICIENCY;
D O I
10.1109/DCC58796.2024.00018
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Generative Face Video Coding (GFVC) techniques can exploit the compact representation of facial priors and the strong inference capability of deep generative models, achieving highquality face video communication in ultra-low bandwidth scenarios. This paper conducts a comprehensive survey on the recent advances of the GFVC techniques and standardization efforts, which could be applicable to ultra low bitrate communication, user-specified animation/filtering and metaverse-related functionalities. In particular, we generalize GFVC systems within one coding framework and summarize different GFVC algorithms with their corresponding visual representations. Moreover, we review the GFVC standardization activities that are specified with supplemental enhancement information messages. Finally, we discuss fundamental challenges and broad applications on GFVC techniques and their standardization potentials, as well as envision their future trends. The project page can be found at https://github.com/Berlin0610/Awesome-Generative-Face-Video-Coding.
引用
收藏
页码:103 / 112
页数:10
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