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We address the problem of deformable shape and motion recovery from point correspondences in multiple perspective images. We use the low-rank shape model, i.e. the 3D shape is represented as a linear combination of unknown shape bases. We propose a new way of looking at the low-rank shape model. Instead of considering it as a whole, we assume a(More)
Fitting a single generic AAM on an unseen face (that is not in the training set) under any pose and expression is very difficult. The variability of the data is so high that the fitting process usually gets stuck into one of the numerous local minima. We show that a solution to this problem consists to separate the variability sources. We build a pool of(More)
This paper deals with shading and AAMs. Shading is created by lighting change. It can be of two types: self-shading and external shading. The effect of self-shading can be explicitly learned and handled by AAMs. This is not however possible for external shading, which is usually dealt with by robustifying the cost function. We take a different approach: we(More)
Automatic extraction of facial feature deformations (either due to identity change or expression) is a challenging task and could be the base of a facial expression interpretation system. We use Active Appearance Models and the simultaneous inverse compositional algorithm to extract facial deformations as a starting point and propose a modified version(More)
An Active Appearance Model (AAM) is a variable shape and appearance model built from annotated training images. It has been largely used to synthesize or fit face images. Person-independent face AAM fitting is a challenging open issue. For standard AAMs, fitting a face image for an individual which is not in the training set is often limited in accuracy,(More)
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