We consider the problem of computing accurate point-to-point correspondences among a set of human bodies in varying postures using a landmark-free approach. The approach learns the locations of the anthropometric landmarks present in a database of human models in strongly varying postures and uses this knowledge to automatically predict the locations of these anthropometric landmarks on a newly available scan. The predicted landmarks are then used to compute point-to-point correspondences between a rigged template model and the newly available scan.
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