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Markerless tracking of dynamic 3D scans of faces

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

/persons/resource/persons83829

Breidt,  M
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
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;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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

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Curio,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
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. (2010). Markerless tracking of dynamic 3D scans of faces. In C. Curio, H. Bülthoff, & M. Giese (Eds.), Dynamic Faces: Insights from Experiments and Computation (pp. 255-276). Cambridge, MA, USA: MIT Press.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-BD3E-3
Zusammenfassung
This chapter presents a novel computer graphics approach suitable for the 3D tracking of facial movements. This kernel-based approach for constructing 3D facial animations also provides information needed for realistic and controllable facial animation and dynamic analyses of face space. This approach, which provides a fully automatic, markerless tracking system, creates a platform where an intersection takes place between psychology and other research areas such as computer graphics and machine learning. The chapter highlights the need to develop a system in the future that can improve the mesh regularization terms in a face-specific manner by using feedback and findings from previous tracking systems.