Facial Expression Parameters Extraction using Graph Convolution Networks

被引：0

作者：

Lee, Hyeong-Geun ^{[1
]}

Hur, Jee-Sic ^{[1
]}

Kim, Jin-Woong ^{[1
]}

Kim, Do-Hyeun ^{[1
]}

Kim, Soo-Kyun ^{[1
]}

机构：

[1] Jeju Natl Univ, Dept Comp Engn, Jeju, South Korea

来源：

2024 FIFTEENTH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS, ICUFN 2024 | 2024年

基金：

新加坡国家研究基金会;

关键词：

Graph Convolution Network; Blendshapes; 3D Facial Animation; Facial Action Cooding System;

D O I：

10.1109/ICUFN61752.2024.10624931

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper addresses a deep learning framework for the extraction of Facial Action Coding System coefficients from 3D facial models. To optimize the labor-intensive process associated with facial animation using traditional Blendshapes, this framework employs a Graph Convolution Network to extract feature vectors from 3D facial models, and accurately infers expression coefficients based on the Facial Action Coding System.

引用

页码：88 / 90

页数：3

共 49 条

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