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Automatic Learning of Articulated Skeletons from 3D Marker Trajectories

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons43977

de Aguiar,  Edilson
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45610

Theobalt,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;
Programming Logics, 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;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45606

Theisel,  Holger
Computer Graphics, MPI for Informatics, Max Planck Society;

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Zitation

de Aguiar, E., Theobalt, C., & Seidel, H.-P. (2006). Automatic Learning of Articulated Skeletons from 3D Marker Trajectories. In Advances in Visual Computing: Second International Symposium, ISVC 2006, Part I (pp. 485-494). Berlin, Germany: Springer.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-2235-4
Zusammenfassung
We present a novel fully-automatic approach for estimating an articulated skeleton of a moving subject and its motion from body marker trajectories that have been measured with an optical motion capture system. Our method does not require a priori information about the shape and proportions of the tracked subject, can be applied to arbitrary motion sequences, and renders dedicated initialization poses unnecessary. To serve this purpose, our algorithm first identifies individual rigid bodies by means of a variant of spectral clustering. Thereafter, it determines joint positions at each time step of motion through numerical optimization, reconstructs the skeleton topology, and finally enforces fixed bone length constraints. Through experiments, we demonstrate the robustness and effciency of our algorithm and show that it outperforms related methods from the literature in terms of accuracy and speed.