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Motion constraint

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Abstract

In this paper, we propose a hybrid postural control approach taking advantage of data-driven and goal-oriented methods while overcoming their limitations. In particular, we take advantage of the latent space characterizing a given motion database. We introduce a motion constraint operating in the latent space to benefit from its much smaller dimension compared to the joint space. This allows its transparent integration into a Prioritized Inverse Kinematics framework. If its priority is high the constraint may restrict the solution to lie within the motion database space. We are more interested in the alternate case of an intermediate priority level that channels the postural control through a spatiotemporal pattern representative of the motion database while achieving a broader range of goals. We illustrate this concept with a sparse database of large range full-body reach motions.

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References

  1. Alexa, M., Mueller, W.: Representing animations by principal components. Comput. Graph. Forum 19, 3 (2000)

    Article  Google Scholar 

  2. Arikan, O., Forsyth, D.A., O’Brien, J.F.: Motion synthesis from annotations. CM Trans. Graph. 22(3), 402–408 (2003)

    Article  Google Scholar 

  3. Aydin, A., Nakajima, M.: Database guided computer animation of human grasping using forward and inverse kinematics. Comput. Graph. 23, 145–154 (1999)

    Article  Google Scholar 

  4. Baerlocher, P., Boulic, R.: An inverse kinematics architecture enforcing an arbitrary number of strict priority levels. Vis. Comput. 20(6), 402–417 (2004)

    Article  Google Scholar 

  5. Berthoz, A.: The Brain’s Sense of Movement. Perspectives in Cognitive Neuroscience. Harvard University Press, Cambridge (2002)

    Google Scholar 

  6. Carvalho, S.R., Boulic, R., Thalmann, D.: Interactive low-dimensional human motion synthesis by combining motion models and PIK. Comput. Animat. Virtual Worlds 18(4–5), 493–503 (2007)

    Article  Google Scholar 

  7. Egges, A., Molet, T., Magnenat-Thalmann, N.: Personalised real-time idle motion synthesis. In: Pacific Graphics, Seoul, Korea, pp. 121–130 (2004)

  8. Glardon, P., Boulic, R., Thalmann, D.: Robust on-line adaptive footplant detection and enforcement for locomotion. Vis. Comput. 22(3), 194–209 (2006)

    Article  Google Scholar 

  9. Gleicher, M.: Retargetting motion to new characters. In: SIGGRAPH ’98: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, pp. 33–42. ACM, New York (1998)

    Chapter  Google Scholar 

  10. Grochow, K., Martin, S.L., Hertzmann, A., Popovic, Z.: Style-based inverse kinematics. In: SIGGRAPH ’04: ACM SIGGRAPH 2004 Papers, pp. 522–531. ACM, New York (2004)

    Chapter  Google Scholar 

  11. Grassia, F.S.: Practical parameterization of rotations using the exponential map. J. Graph. Tools 3(3), 29–48 (1998)

    Google Scholar 

  12. Grassia, F.S.: Believable automatically synthesized motion by knowledge-enhanced motion transformation, PhD Thesis, Pittsburgh, PA, USA (2003)

  13. Howe, N.R., Levention, M.E., Freeman, W.T.: Bayesian reconstruction of 3d human motion from single-camera vide. In: Advances in Neural Information Processing Systems 12, pp. 820–826. MIT Press, Cambridge (2000)

    Google Scholar 

  14. Jolliffe, I.T.: Principal Component Analysis. Springer, Berlin (1986)

    Google Scholar 

  15. Korein, J.U.: A Geometric Investigation of Reach. MIT Press, Cambridge (1985)

    MATH  Google Scholar 

  16. Kovar, L., Gleicher, M.: Automated extraction and parameterization of motions in large data sets. ACM Trans. Graph. 23(3), 559–568 (2004)

    Article  Google Scholar 

  17. Kulpa, R., Multon, F., Arnaldi, B.: Morphology-independent representation of motion for interactive human-like animation. Comput. Graph. Forum 24, 343–352 (2005)

    Article  Google Scholar 

  18. Lee, J., Chai, J., Reitsma, P., Hodgins, J.K., Pollard, N.: Interactive control of avatars animated with human motion data. ACM Trans. Graph. 21(3), 491–500 (2002)

    Google Scholar 

  19. Mukai, T., Kuriyama, S.: Geostatistical motion interpolation. ACM Trans. Graph. 24(3), 1062–1070 (2005)

    Article  Google Scholar 

  20. Nakamura, Y., Hanafusa, H., Yoshikawa, T.: Inverse kinematic solutions with singularity robustness for robot manipulator control. J. Dyn. Syst. Meas. Control 108, 163–171 (1986)

    Article  MATH  Google Scholar 

  21. Park, W., Chaffin, D.B., Martin, B.J.: Toward memory-based human motion simulation: development and validation of a motion modification algorithm. IEEE Trans. Syst. Man Cybern., Part A 34(3), 376–386 (2004)

    Article  Google Scholar 

  22. Popovic, Z., Witkin, A.: Physically based motion transformation. In: SIGGRAPH ’99: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 11–20. ACM, New York (1999)

    Chapter  Google Scholar 

  23. Rose, C.F., III, Sloan, P.-P.J., Cohen, M.F.: Artist-directed inverse-kinematics using radial basis function interpolation. Comput. Graph. Forum 20, 3 (2001)

    Article  Google Scholar 

  24. Safanova, A., Hodgins, J.K., Pollard, N.S.: Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. ACM Trans. Graph. 23(3), 514–521 (2004)

    Article  Google Scholar 

  25. Shin, H.J., Lee, J.: Motion synthesis and editing in low-dimensional spaces, Research articles. Comput. Animat. Virtual Worlds 17(3–4), 219–227 (2006)

    Article  Google Scholar 

  26. Wang, X.: Three-dimensional kinematic analysis of influence of hand orientation and joint limits on the control of arm posture and movement. Biol. Cybern. 80(6), 449–463 (1999)

    Article  Google Scholar 

  27. Wang, X.: Behavior-based inverse kinematics algorithm to predict arm prehension postures for computer-aided ergonomic evaluation. J. Biomech. 32(5), 453–460 (1999)

    Article  Google Scholar 

  28. Weisstein, E.W.: Barycentric Coordinates, Mathworld—A Wolfram Web Resource

  29. Yamane, K., Kuffner, J.J., Hodgins, J.K.: Synthesizing animations of human manipulation tasks. ACM Trans. Graph. 23(3), 532–539 (2004)

    Article  Google Scholar 

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Correspondence to Daniel Raunhardt.

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This work has been supported by the Swiss National Foundation under the grant N° 200020-117706.

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Raunhardt, D., Boulic, R. Motion constraint. Vis Comput 25, 509–518 (2009). https://doi.org/10.1007/s00371-009-0336-2

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