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Natural Pose Generation from a Reduced Dimension Motion Capture Data Space

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Advances in Visual Computing (ISVC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5875))

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Abstract

Human animation from motion capture data is typically limited to whatever movement was performed by the actor. A method to create a wider range of motion in the animation utilizes the motion capture database to synthesize new poses. This paper proposes a method to generate original natural poses based on the characteristics of natural poses based on motion capture data. Principal Component Analysis is used to transform the data into a reduced dimensional space. An unconstrained pose data set is created by calculating the position of the human skeleton based on the reduced dimensional space. Constrained pose data can be created using interpolation and iteration on the unconstrained pose data. We show some example results of the generated poses and compare these poses to poses created with iterative inverse kinematics methods. Results show that our method is more accurate and more natural than iterative inverse kinematics methods.

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Author information

Authors and Affiliations

  1. Media and Entertainment Technologies Laboratory, Computer Science Department, Michigan State University,  

    Reza Ferrydiansyah & Charles B. Owen

Authors
  1. Reza Ferrydiansyah
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  2. Charles B. Owen
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, 338-8570, Saitama, Japan

    Yoshinori Kuno

  6. Institute for Infocomm Research, 21 Heng Mui Keng Terrace, P.O. Box, 119613, Singapore

    Junxian Wang

  7. Department of Computer Science & Information Engineering, Tamkang University, Tamsui, Taipei, Taiwan, R.O.C.

    Jun-Xuan Wang

  8. Microsoft Research, Redmond, WA, USA

    Junxian Wang

  9. Department of Informatics, Univ. of Zurich, Winterthurerstr. 190, P.O. Box, 8057, Zurich, Switzerland

    Renato Pajarola

  10. Lawrence Livermore National Laboratory, 94550, Livermore, CA, USA

    Peter Lindstrom

  11. University of Applied Sciences Bonn-Rhein-Sieg, 53754, Sankt Augustin, Germany

    André Hinkenjann

  12. Humana Inc., 40202, Louisville, KY, USA

    Miguel L. Encarnação

  13. SCI Institute & School of Computing, University of Utah, 84112, Salt Lake City, UT, USA

    Cláudio T. Silva

  14. Desert Research Institute, 89512, Reno, NV, USA

    Daniel Coming

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© 2009 Springer-Verlag Berlin Heidelberg

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Ferrydiansyah, R., Owen, C.B. (2009). Natural Pose Generation from a Reduced Dimension Motion Capture Data Space. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_49

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  • DOI: https://doi.org/10.1007/978-3-642-10331-5_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10330-8

  • Online ISBN: 978-3-642-10331-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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