Learning Dense 3D Correspondence

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

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Florian Steinke, Volker Blanz, Bernhard Schölkopf


Establishing correspondence between distinct objects is an important and nontrivial task: correctness of the correspondence hinges on properties which are difficult to capture in an a priori criterion. While previous work has used a priori criteria which in some cases led to very good results, the present paper explores whether it is possible to learn a combination of features that, for a given training set of aligned human heads, characterizes the notion of correct correspondence. By optimizing this criterion, we are then able to compute correspondence and morphs for novel heads.