Generative Face Video Coding Techniques and Standardization Efforts: A
  Review
Bolin Chen Jie Chen Shiqi Wang Yan Ye
Generative Face Video Coding (GFVC) techniques can exploit the compact
representation of facial priors and the strong inference capability of deep
generative models, achieving high-quality 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.