Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61982
Title: Data-driven facial animation via semi-supervised local patch alignment
Authors: Zhang, J
Yu, J
You, J 
Tao, D
Li, N
Cheng, J
Keywords: Facial animation
Global alignment
Linear transformation
Local patch
Manifold
Issue Date: 2016
Publisher: Elsevier
Source: Pattern recognition, 2016, v. 57, p. 1-20 How to cite?
Journal: Pattern recognition 
Abstract: This paper reports a novel data-driven facial animation technique which drives a neutral source face to get the expressive target face using a semi-supervised local patch alignment framework. We define the local patch and assume that there exists a linear transformation between a patch of the target face and the intrinsic embedding of the corresponding patch of the source face. Based on this assumption, we compute the intrinsic embeddings of source patches and align these embeddings to form the result. During the course of alignment, we use a set of motion data as shape regularizer to impel the result to approach the unknown target face. The intrinsic embedding can be computed through both locally linear embedding and local tangent space alignment. Experimental results indicate that the proposed framework can obtain decent face driving results. Quantitative and qualitative evaluations of the proposed framework demonstrate its superiority to existing methods.
URI: http://hdl.handle.net/10397/61982
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2016.02.021
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