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Markerless 3D Face Tracking

MPG-Autoren
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Walder,  C
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Cognitive Engineering, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Breidt,  M
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Cognitive Engineering, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83839

Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83871

Curio,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Cognitive Engineering, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Walder, C., Breidt, M., Bülthoff, H., Schölkopf, B., & Curio, C. (2009). Markerless 3D Face Tracking. In J. Denzler, G. Notni, & H. Süsse (Eds.), Pattern Recognition: 31st DAGM Symposium, Jena, Germany, September 9-11, 2009 (pp. 41-50). Berlin, Germany: Springer.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-C311-9
Zusammenfassung
We present a novel algorithm for the markerless tracking of deforming surfaces such as faces. We acquire a sequence of 3D scans along with color images at 40Hz. The data is then represented by implicit surface and color functions, using a novel partition-of-unity type method of efficiently combining local regressors using nearest neighbor searches. Both these functions act on the 4D space of 3D plus time, and use temporal information to handle the noise in individual scans. After interactive registration of a template mesh to the first frame, it is then automatically deformed to track the scanned surface, using the variation
of both shape and color as features in a dynamic energy minimization
problem. Our prototype system yields high-quality animated 3D models in correspondence, at a rate of approximately twenty seconds per
timestep. Tracking results for faces and other objects are presented.