Semantic Face Compression for Metaverse: A Compact 3D Descriptor Based Approach
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Abstract (arXiv)
In this letter, we envision a new metaverse communication paradigm for virtual avatar faces, and develop the semantic face compression with compact 3D facial descriptors. The fundamental principle is that the communication of virtual avatar faces primarily emphasizes the conveyance of semantic information. In light of this, the proposed scheme offers the advantages of being highly flexible, efficient and semantically meaningful. The semantic face compression, which allows the communication of the descriptors for artificial intelligence based understanding, could facilitate numerous applications without the involvement of humans in metaverse. The promise of the proposed paradigm is also demonstrated by performance comparisons with the state-of-the-art video coding standard, Versatile Video Coding. A significant improvement in terms of rate-accuracy performance has been achieved. The proposed scheme is expected to enable numerous applications, such as digital human communication based on machine analysis, and to form the cornerstone of interaction and communication in the metaverse.
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References · 17
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
- 2018Variational image compression with a scale hyperpriorcited by 935
- 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 903
- 2017VoxCeleb: a large-scale speaker identification datasetcited by 732
- 2020First Order Motion Model for Image Animationcited by 704
- 2016End-to-end Optimized Image Compressioncited by 696
- 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 492
- 2014Learning Face Representation from Scratchcited by 453
- 2020One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencingcited by 444
- 2018DVC: An End-to-end Deep Video Compression Frameworkcited by 332
5 more
- 2020M-LVC: Multiple Frames Prediction for Learned Video Compressioncited by 107
- 2021Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognitioncited by 55
- 2022Coarse-to-fine Deep Video Coding with Hyperprior-guided Mode Predictioncited by 55
- 2022AlphaVC: High-Performance and Efficient Learned Video Compressioncited by 28
- 2021Weakly-Supervised Multi-Face 3D Reconstructioncited by 5
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