Clustering and classifying deformations for Shape Regression applied to the human body

In this paper, we present an approach to the problem of aligning a Point Distribution Model onto the human body. The key idea of this paper is the clustering of the shape displacements into classes, in order to group the residual deformations in a coherent direction according to the image. We combine this approach by employing the implicitly encoded constraints and the double cascade pattern defined in the “Explicit Shape Regression”. We proceed with adjustments adapted for the context of human body. Hence, we reformulate a regression problem into a classification of displacements. We show through our experiments that obtaining minimal classification errors leads effectively to faster and more accurate regression results. We enhance this classification process by conjointly transforming residual displacements and images to train our classifiers.