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  Learning Dense 3D Correspondence

Steinke, F., Schölkopf, B., & Blanz, V. (2007). Learning Dense 3D Correspondence. Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, 1313-1320.

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 Creators:
Steinke, F1, Author           
Schölkopf, B1, Author           
Blanz, V2, Author           
Schölkopf, Editor
B., Editor
Platt, J., Editor
Hofmann, T., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              

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 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.

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 Dates: 2007-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-19568-2
URI: http://nips.cc/Conferences/2006/
BibTex Citekey: 4148
 Degree: -

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Title: Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006)
Place of Event: Vancouver, BC, Canada
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Title: Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference
Source Genre: Journal
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Affiliations:
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1313 - 1320 Identifier: -