We present a new technique for image-based modeling using as input image contours and a deformable 3D template. The technique gradually deforms the template to fit the contours. At the heart of this process is the need to provide good correspondences between points on image contours and vertices on the model. We propose the use of a hidden Markov model for efficiently computing an optimal set of correspondences. An iterative match-and-deform process then progressively deforms the 3D template to match the image contours. The technique can successfully deform the template to match contours that represent significant changes in shape. The template models can be augmented to include properties such as bending stiffness and symmetry constraints. We demonstrate the results on a variety of objects.
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