Generative Face Video Coding Techniques and Standardization Efforts: A Review
This is an incremental survey paper that synthesizes existing research for researchers and practitioners in video compression and communication.
This paper reviews Generative Face Video Coding (GFVC) techniques, which use facial priors and deep generative models to enable high-quality face video communication at ultra-low bandwidth, and summarizes their standardization efforts and applications.
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.