Human Motion Reconstruction and Synthesis of Human Skills

Conference paper


Reconstructing human motion dynamics in real-time is a challenging problem since it requires accurate motion sensing, subject specific models, and efficient reconstruction algorithms. A promising approach is to construct accurate human models, and control them to behave the same way the subject does. Here, we demonstrate that the whole-body control approach can efficiently reconstruct a subject’s motion dynamics in real world task-space when given a scaled model and marker based motion capture data. We scaled a biomechanically realistic musculoskeletal model to a subject, captured motion with suitably placed markers, and used an operational space controller to directly track the motion of the markers with the model. Our controller tracked the positions, velocities, and accelerations of many markers in parallel by assigning them to tasks with different priority levels based on how free their parent limbs were. We executed lower priority marker tracking tasks in the successive null spaces of the higher priority tasks to resolve their interdependencies. The controller accurately reproduced the subject’s full body dynamics while executing a throwing motion in near real time. Its reconstruction closely matched the marker data, and its performance was consistent for the entire motion. Our findings suggest that the direct marker tracking approach is an attractive tool to reconstruct and synthesize the dynamic motion of humans and other complex articulated body systems in a computationally efficient manner.

Key words

Motion reconstruction marker space control musculoskeletal model human motion synthesis 


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  1. 1.
    Lee J., Shin S.Y., A hierarchical approach to interactive motion editing for human-like figures. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques. ACM Press/Addison Wesley Publishing, pp. 39–48 (1999).CrossRefGoogle Scholar
  2. 2.
    Choi, K., Ko, H., On-line motion retargetting. In: Proceedings Seventh Pacific Conference on Computer Graphics and Applications. IEEE Computer Society, p. 32 (1999).CrossRefGoogle Scholar
  3. 3.
    Savenko, A., Clapworthy, G., Using motion analysis techniques for motion retargetting. In: Proceedings Sixth International Conference on Information Visualization, Vol. IV. IEEE Computer Society Press, p. 110 (2002).CrossRefGoogle Scholar
  4. 4.
    Nakamura, Y., Yamane, K., Suzuki, I., Fujita, Y., Dynamic computation of musculo-skeletal human model based on efficient algorithm for closed kinematic chains. In: Proceedings of the 2nd International Symposium on Adaptive Motion of Animals and Machines. Springer, New York (2003).Google Scholar
  5. 5.
    Grochow, K., Martin, S.L., Hertzmann, A., Popovic, Z., Style-based inverse kinematics. In: ACM Transactions on Graphics (TOG), Proceedings of the 2004 SIGGRAPH Conference, 23(3), 522–531 (2004).CrossRefGoogle Scholar
  6. 6.
    Nakamura, Y., Yamane, K., Suzuki, I., Fujita, Y.: Somatosensory computation for man-machine interface from motion capture data and musculoskeletal human model. IEEE Transactions on Robotics, 21(1), 58–66 (2005).CrossRefGoogle Scholar
  7. 7.
    Dariush, B., Gienger, M., Jian, B., Goerick, C., and Fujimura, K., Whole body humanoid control from human motion descriptors. In: IEEE Int. Conf. on Robotics and Automation, pp. 2677–2684 (2008).CrossRefGoogle Scholar
  8. 8.
    Demircan, E., Sentis, L., De Sapio, V., Khatib, O., Human motion reconstruction by direct control of marker trajectories. In: Lenarčič, J., Wenger, P. (Eds.), Advances in Robot Kinematics: Analysis and Design. Springer, Dordrecht, pp. 263–272 (2008).CrossRefGoogle Scholar
  9. 9.
    Khatib, O., Demircan, E., DeSapio, V., Sentis, L., Besier, T., Delp, S.: Robotics-based synthesis of human motion. Journal of Physiology, Paris, 103, 211–219 (2009).CrossRefGoogle Scholar
  10. 10.
    Delp, S.L., Loan, J.P., Hoy, M.G., Zajac, F.E., Topp, E.L., Rosen, J.M.: An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures. IEEE Transactions on Biomedical Engineering 37, 757–767 (1990).CrossRefGoogle Scholar
  11. 11.
    Holzbaur, K.R.S., Murray, W.M., Delp, S.L: A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control. Ann. of Biomed. Eng. 33, 829– 840 (2005).CrossRefGoogle Scholar
  12. 12.
    Khatib, O., Brock, O., Chang, K., Conti, F., Ruspini, D., Sentis, L.: Robotics and interactive simulation. Communications of the ACM, 45(3), 46–51 (2002).CrossRefGoogle Scholar
  13. 13.
    Khatib, O., Sentis, L., Park, J., Warren, J.: Whole-body dynamic behavior and control of human-like robots. International Journal of Humanoid Robotics, 1(1), 29–43 (2004).CrossRefGoogle Scholar
  14. 14.
    Sentis, L., Khatib, O.: Synthesis of whole-body behaviors through hierarchical control of behavioral primitives. International Journal of Humanoid Robotics, 2(4), 505–518 (2005).CrossRefGoogle Scholar
  15. 15.
    Dempster, W.T.: Space requirements of the seated operator. WADC Technical Report, Wright- Patterson Air Force Base, OH, pp. 55–159 (1955).Google Scholar
  16. 16.
    Khatib, O.: A unified approach for motion and force control of robot manipulators: The operational space formulation. International Journal of Robotics Research, 3(1), 43–53 (1987).Google Scholar
  17. 17.
    Khatib, O.: Inertial properties in robotic manipulation: An object level framework. International Journal of Robotics Research, 14(1), 19–36 (1995).CrossRefGoogle Scholar
  18. 18.
    Besier, T.F., Lloyd, D.G., Ackland, T.R., Cochrane, J.L.: External loading of the knee joint during running and cutting manoeuvres. Medicine in Science and Sports and Exercise, 33(7), 1168–1175 (2001).CrossRefGoogle Scholar
  19. 19.
    Besier, T.F., Lloyd, D.G., Ackland, T.R., Cochrane, J.L.: Anticipatory effects on knee joint loading during running and cutting manoeuvres. Medicine in Science and Sports and Exercise, 33(7), 1176–1181 (2001).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Artificial Intelligence Lab.StanfordUSA
  2. 2.Human Performance Lab.Stanford UniversityStanfordU.S.A.

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