Hierarchical Implicit Surface Joint Limits to Constrain Video-Based Motion Capture

  • Lorna Herda
  • Raquel Urtasun
  • Pascal Fua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3022)


To increase the reliability of existing human motion tracking algorithms, we propose a method for imposing limits on the underlying hierarchical joint structures in a way that is true to life. Unlike most existing approaches, we explicitly represent dependencies between the various degrees of freedom and derive these limits from actual experimental data.

To this end, we use quaternions to represent individual 3 DOF joint rotations and Euler angles for 2 DOF rotations, which we have experimentally sampled using an optical motion capture system. Each set of valid positions is bounded by an implicit surface and we handle hierarchical dependencies by representing the space of valid configurations for a child joint as a function of the position of its parent joint.

This representation provides us with a metric in the space of rotations that readily lets us determine whether a posture is valid or not. As a result, it becomes easy to incorporate these sophisticated constraints into a motion tracking algorithm, using standard constrained optimization techniques. We demonstrate this by showing that doing so dramatically improves performance of an existing system when attempting to track complex and ambiguous upper body motions from low quality stereo data.


Euler Angle Motion Capture Elbow Joint Implicit Surface Joint Limit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Gavrila, D.: The Visual Analysis of Human Movement: A Survey. Computer Vision and Image Understanding 73 (1999)Google Scholar
  2. 2.
    Moeslund, T., Granum, E.: Pose estimation of a human arm using kinematic constraints. In: Scandinavian Conference on Image Analysis, Bergen, Norway (2001)Google Scholar
  3. 3.
    Moeslund, T.: Computer Vision-Based Motion Capture of Body Language. PhD thesis, Aalborg University, Aalborg, Denmark (2003)Google Scholar
  4. 4.
    Rehg, J.M., Morris, D.D., Kanade, T.: Ambiguities in Visual Tracking of Articulated Objects using 2–D and 3–D Models. International Journal of Robotics Research 22, 393–418 (2003)CrossRefGoogle Scholar
  5. 5.
    Bregler, C., Malik, J.: Tracking People with Twists and Exponential Maps. In: Conference on Computer Vision and Pattern Recognition, Santa Barbara, CA (1998)Google Scholar
  6. 6.
    Demirdjian, D.: Enforcing constraints for human body tracking. In: Workshop on Multi-Object Tracking (2003)Google Scholar
  7. 7.
    Sminchisescu, C., Triggs, B.: Estimating articulated human motion with covariance scaled sampling. International Journal of Robotics Research (2003)Google Scholar
  8. 8.
    Engin, A., Tümer, S.: Three-dimensional kinematic modeling of the human shoulder complex. Journal of Biomechanical Engineering 111, 113–121 (1989)CrossRefGoogle Scholar
  9. 9.
    Herda, L., Urtasun, R., Hanson, A., Fua, P.: An automatic method for determining quaternion field boundaries for ball-and-socket joint limits. International Journal of Robotics Research 22, 419–436 (2003)CrossRefGoogle Scholar
  10. 10.
    Shoemake, K.: Animating Rotation with Quaternion Curves. Computer Graphics, SIGGRAPH Proceedings 19, 245–254 (1985)CrossRefGoogle Scholar
  11. 11.
    Baerlocher, P., Boulic, R.: An Inverse Kinematics Architecture for Enforcing an Arbitrary Number of Strict Priority Levels. The Visual Computer (2004)Google Scholar
  12. 12.
    Plänkers, R., Fua, P.: Articulated Soft Objects for Multi-View Shape and Motion Capture. IEEE Transactions on Pattern Analysis and Machine Intelligence (2003)Google Scholar
  13. 13.
    Hatze, H.: A three-dimensional multivariate model of passive human joint torques and articular boundaries. Clinical Biomechanics 12, 128–135 (1997)CrossRefGoogle Scholar
  14. 14.
    Kodek, T., Munich, M.: Identifying Shoulder and Elbow Passive Moments and Muscle Contributions. In: International Conference on Intelligent Robots and Systems (2002)Google Scholar
  15. 15.
    Johnston, R., Smidt, G.: Measurement of hip joint motion during walking. Journal of Bone and Joint Surgery 51, 1083–1094 (1969)Google Scholar
  16. 16.
    Meskers, C., Vermeulen, H., de Groot, J., der Helm, F.V., Rozing, P.: 3d shoulder position measurements using a six-degree-of-freedom electromagnetic tracking device. Clinical Biomechanics 13, 280–292 (1998)CrossRefGoogle Scholar
  17. 17.
    der Helm, F.V.: A standardized protocol for motion recordings of the shoulder. In: Conference of the International Shoulder Group, Masstritcht, Netherlands (1997)Google Scholar
  18. 18.
    Bao, H., Willems, P.: On the kinematic modelling and the parameter estimation of the human shoulder. Journal of Biomechanics 32, 943–950 (1999)CrossRefGoogle Scholar
  19. 19.
    Maurel, W.: 3d modeling of the human upper limb including the biomechanics of joints, muscles and soft tissues (1998)Google Scholar
  20. 20.
    Wang, X., Maurin, M., Mazet, F., Maia, N.D.C., Voinot, K., Verriest, J., Fayet, M.: Three-dimensional modelling of the motion range of axial rotation of the upper arm. Journal of Biomechanics 31, 899–908 (1998)CrossRefGoogle Scholar
  21. 21.
    Bobick, N.: Rotating objects using quaternions. Game Developer 2(26) (1998)Google Scholar
  22. 22.
    Watt, A., Watt, M.: Advanced animation and rendering techniques (1992)Google Scholar
  23. 23.
    Grassia, F.: Practical parameterization of rotations using the exponential map. Journal of Graphics Tools 3, 29–48 (1998)Google Scholar
  24. 24.
    Pervin, E., Webb, J.: Quaternions for computer vision and robotics. In: Conference on Computer Vision and Pattern Recognition, Washington, D.C., pp. 382–383 (1983)Google Scholar
  25. 25.
    Faugeras, O.: Three-Dimensional Computer Vision: a Geometric Viewpoint. MIT Press, Cambridge (1993)Google Scholar
  26. 26.
    Bloomenthal, J.: Calculation of reference frames along a space curve. In: Glassner, A. (ed.) Graphics Gems, pp. 567–571. Academic Press, Cambridge (1990)Google Scholar
  27. 27.
    Tsingos, N., Bittar, E., Gascuel, M.: Implicit surfaces for semi-automatic medical organs reconstruction. In: Computer Graphics International, Leeds, UK, pp. 3–15 (1995)Google Scholar
  28. 28.
    Lorensen, W., Cline, H.: Marching Cubes: A High Resolution 3D Surface Construction Algorithm. In: Computer Graphics, SIGGRAPH Proceedings, vol. 21, pp. 163–169 (1987)Google Scholar
  29. 29.
    Ranjan, V., Fournier, A.: Shape transformations using union of spheres. Technical Report TR-95-30, Department of Computer Science, University of British Columbia (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Lorna Herda
    • 1
  • Raquel Urtasun
    • 1
  • Pascal Fua
    • 1
  1. 1.Computer Vision LaboratoryEPFLLausanneSwitzerland

Personalised recommendations