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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,...
Auto-encoders are one kinds of promising non-probabilistic representation learning paradigms that can efficiently learn stable deterministic features. Recently, auto-encoder algorithms are drawing more and more attentions because of its attractive performance in learning insensitive representation with respect to data changes. The most representative auto-encoder algorithms are the regularized auto-encoders...
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