Learning Dense 3D Correspondence
Florian Steinke, Bernhard Schölkopf and Volker Blanz
In: 20th Annual Conference on Neural Information Processing Systems, 4-9 Dec 2006, Vancouver / Whistler, Canada.
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.