Generative Face Video Coding Techniques and Standardization Efforts: A\n Review

Bolin Chen, Jie Chen, Shiqi Wang,Yan Ye

2024 Data Compression Conference (DCC)(2023)

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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 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.
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关键词
Source Code,Video Coding,Standardization Efforts,Deep Models,Visual Representation,Bitrate,Compact Representation,Deep Generative Models,High-quality Video,Project Page,Neural Network,Decoding,General Method,Interoperability,Generative Adversarial Networks,Test Sequences,Quality Metrics,Face Images,Peak Signal-to-noise Ratio,Software Implementation,Reconstruction Quality,2D Keypoints,Video Compression,Mobile Platform,Live Broadcast,Face Representation,Variational Autoencoder,Series Of Parameters,Visible Face
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