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Konferenzbeitrag

Silhouette Based Generic Model Adaptation for Marker-Less Motion Capturing

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45577

Sunkel,  Martin
Computer Graphics, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45312

Rosenhahn,  Bodo
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45449

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

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Zitation

Sunkel, M., Rosenhahn, B., & Seidel, H.-P. (2007). Silhouette Based Generic Model Adaptation for Marker-Less Motion Capturing. In A. Elgammal, B. Rosenhahn, & R. Klette (Eds.), Human Motion – Understanding, Modeling, Capture and Animation: Second Workshop, Human Motion 2007 (pp. 119-135). Berlin, Germany: Springer.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-20B1-F
Zusammenfassung
This work presents a marker-less motion capture system that incorporates an approach to smoothly adapt a generic model mesh to the individual shape of a tracked person. This is done relying on extracted silhouettes only. Thus, during the capture process the 3D model of a tracked person is learned. Depending on a sparse number of 2D-3D correspondences, that are computed along normal directions from image sequences of different cameras, a Laplacian mesh editing tool generates the final adapted model. With the increasing number of frames an approach for temporal coherence reduces the effects of insufficient correspondence data to a minimum and guarantees smooth adaptation results. Further, we present experiments on non-optimal data that show the robustness of our algorithm.