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Facial Expression Synthesis using a Global‐Local Multilinear Framework
Author(s) -
Wang M.,
Bradley D.,
Zafeiriou S.,
Beeler T.
Publication year - 2020
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13926
Subject(s) - multilinear map , computer science , leverage (statistics) , facial expression , expression (computer science) , computer graphics , artificial intelligence , facial expression recognition , graphics , face (sociological concept) , pattern recognition (psychology) , computer vision , machine learning , facial recognition system , computer graphics (images) , mathematics , social science , sociology , pure mathematics , programming language
We present a practical method to synthesize plausible 3D facial expressions for a particular target subject. The ability to synthesize an entire facial rig from a single neutral expression has a large range of applications both in computer graphics and computer vision, ranging from the efficient and cost‐effective creation of CG characters to scalable data generation for machine learning purposes. Unlike previous methods based on multilinear models, the proposed approach is capable to extrapolate well outside the sample pool, which allows it to plausibly predict the identity of the target subject and create artifact free expression shapes while requiring only a small input dataset. We introduce global‐local multilinear models that leverage the strengths of expression‐specific and identity‐specific local models combined with coarse motion estimations from a global model. Experimental results show that we achieve high‐quality, plausible facial expression synthesis results for an individual that outperform existing methods both quantitatively and qualitatively.