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Video‐driven state‐aware facial animation
Author(s) -
Zeng Ming,
Liang Lin,
Liu Xinguo,
Bao Hujun
Publication year - 2012
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.1455
Subject(s) - computer science , avatar , computer facial animation , animation , feature (linguistics) , expression (computer science) , facial expression , artificial intelligence , face (sociological concept) , set (abstract data type) , point (geometry) , local binary patterns , computer vision , computer animation , support vector machine , facial motion capture , active appearance model , pattern recognition (psychology) , face detection , facial recognition system , computer graphics (images) , human–computer interaction , image (mathematics) , histogram , social science , linguistics , philosophy , geometry , mathematics , sociology , programming language
It is important in computer animation to synthesize expressive facial animation for avatars from videos. Some traditional methods track a set of semantic feature points on the face to drive the avatar. However, these methods usually suffer from inaccurate detection and sparseness of the feature points and fail to obtain high‐level understanding of facial expressions, leading to less expressive and even wrong expressions on the avatar. In this paper, we propose a state‐aware synthesis framework. Instead of simply fitting 3D face to the 2D feature points, we use expression states obtained by a set of low‐cost classifiers (based on local binary pattern and support vector machine) on the face texture to guide the face fitting procedure. Our experimental results show that the proposed hybrid framework enjoys the advantages of the original methods based on feature point and the awareness of the expression states of the classifiers and thus vivifies and enriches the face expressions of the avatar. Copyright © 2012 John Wiley & Sons, Ltd.