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Learning Dense 3D Correspondence
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.
@inproceedings{4148, title = {Learning Dense 3D Correspondence}, journal = {Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference}, booktitle = {Advances in Neural Information Processing Systems 19}, abstract = {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.}, pages = {1313-1320}, editors = {B Sch{\"o}lkopf and J Platt and T Hofmann}, publisher = {MIT Press}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Cambridge, MA, USA}, month = sep, year = {2007}, slug = {4148}, author = {Steinke, F. and Sch{\"o}lkopf, B. and Blanz, V.}, month_numeric = {9} }