Hao Tang, Yun Fu, J. Tu, Thomas S. Huang, M. Hasegawa-Johnson
{"title":"EAVA: A 3D Emotive Audio-Visual Avatar","authors":"Hao Tang, Yun Fu, J. Tu, Thomas S. Huang, M. Hasegawa-Johnson","doi":"10.1109/WACV.2008.4544003","DOIUrl":null,"url":null,"abstract":"Emotive audio-visual avatars have the potential of significantly improving the quality of Human-Computer Interaction (HCI). In this paper, the various technical approaches of a novel framework leading to a text-driven 3D Emotive Audio-Visual Avatar (EAVA) are proposed. Primary work is focused on 3D face modeling, realistic emotional facial expression animation, emotive speech synthesis, and the co-articulation of speech gestures (i.e., lip movements due to speech production) and facial expressions. Experimental results clearly indicate that a certain degree of naturalness and expressiveness has been achieved by EAVA in both audio and visual aspects. Promising potential improvements can be expected by incorporating various data-driven statistical learning models into the framework.","PeriodicalId":439571,"journal":{"name":"2008 IEEE Workshop on Applications of Computer Vision","volume":"111 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE Workshop on Applications of Computer Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WACV.2008.4544003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
Emotive audio-visual avatars have the potential of significantly improving the quality of Human-Computer Interaction (HCI). In this paper, the various technical approaches of a novel framework leading to a text-driven 3D Emotive Audio-Visual Avatar (EAVA) are proposed. Primary work is focused on 3D face modeling, realistic emotional facial expression animation, emotive speech synthesis, and the co-articulation of speech gestures (i.e., lip movements due to speech production) and facial expressions. Experimental results clearly indicate that a certain degree of naturalness and expressiveness has been achieved by EAVA in both audio and visual aspects. Promising potential improvements can be expected by incorporating various data-driven statistical learning models into the framework.