Multidimensional morphable models

We describe a flexible model for representing images of objects of a certain class, known a priori, such as faces, and introduce a new algorithm for matching it to a novel image and thereby performing image analysis. We call this model a multidimensional morphable model or just a, morphable model. The morphable model is learned from example images (called prototypes) of objects of a class. In this paper we introduce an effective stochastic gradient descent algorithm that automaticaIly matches a model to a novel image by finding the parameters that minimize the error between the image generated by the model and the novel image. Two examples demonstrate the robustness and the broad range of applicability of the matching algorithm and the underlying morphable model. Our approach can provide novel solutions to several vision tasks, including the computation of image correspondence, object verification, image synthesis and image compression.