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Generative Face Video Coding Techniques and Standardization Efforts: A Review

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

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. © 2024 IEEE.
Original languageEnglish
Title of host publicationProceedings - DCC 2024: 2024 Data Compression Conference
EditorsAli Bilgin, James E. Fowler, Joan Serra-Sagrista, Yan Ye, James A. Storer
PublisherIEEE
Pages103-112
ISBN (Electronic)979-8-3503-8587-8
ISBN (Print)979-8-3503-8588-5
DOIs
Publication statusPublished - 2024
Event2024 Data Compression Conference (DCC 2024) - Snowbird, United States
Duration: 19 Mar 202422 Mar 2024

Publication series

Name
ISSN (Print)1068-0314
ISSN (Electronic)2375-0359

Conference

Conference2024 Data Compression Conference (DCC 2024)
PlaceUnited States
CitySnowbird
Period19/03/2422/03/24

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62022002, in part by the Hong Kong Research Grants Council General Research Fund 11203220, in part by the Innovation and Technology Fund Project GHP/044/21SZ, and in part by the Alibaba Innovative Research.

RGC Funding Information

  • RGC-funded

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