The Visual Computer

, Volume 29, Issue 3, pp 171–188 | Cite as

Latent motion spaces for full-body motion editing

  • Schubert R. CarvalhoEmail author
  • Ronan Boulic
  • Creto A. Vidal
  • Daniel Thalmann
Original Article


We explore an approach to full-body motion editing with linear motion models, prioritized constraint-based optimization and latent-space interpolation. By exploiting the mathematical connections between linear motion models and prioritized inverse kinematics (PIK), we formulate and solve the motion editing problem as an optimization function whose differential structure is rich enough to efficiently optimize user-specified constraints within the latent motion space. Performing motion editing within latent motion spaces has the advantage of handling pose transitions and consequently motion flow by construction from single key-frame editing. To handle motion adjustments from multiple key-frame and trajectory constraints, we developed a latent-space interpolation technique by exploiting spline functions. Such an approach handles per-frame adjustments generating smooth animations, while avoiding the computational expense of joint space interpolations. We demonstrate the usefulness of this approach by editing and generating full-body reaching and walking jump animations in challenging environment scenarios.


Linear motion models Constraint-based optimization Latent interpolation Motion editing 



The authors would like to thank Mireille Clavien for the video production; Autodesk/Maya for their donation of Maya software; Benoît Le Callennec for providing access to his motion editing system (with the support of the SNF grant n o 200020-109989); and the valuable suggestions of all the anonymous reviewers. This work was supported by the EPFL—Sport and Rehabilitation Engineering program. The third author would like to acknowledge CAPES/Brazil for the grant 4557/06-9 that helped support him in VRlab-EPFL Switzerland during the academic year 2007–2008.

Supplementary material

(MP4 13.2 MB)

(MP4 13.0 MB)


  1. 1.
    Arikan, O., Forsyth, D.: Interactive motion generation from examples. In: SIGGRAPH’02: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, pp. 483–490. ACM, New York (2002). doi: CrossRefGoogle Scholar
  2. 2.
    Baerlocher, P., Boulic, R.: An inverse kinematic architecture enforcing an arbitrary number of strict priority levels. Vis. Comput. 20(6) (2004) Google Scholar
  3. 3.
    Callennec, B.L., Boulic, R.: Interactive motion deformation with prioritized constraints. Graph. Models 68, 175–193 (2006). Special Issue on SCA 2004 CrossRefGoogle Scholar
  4. 4.
    Carvalho, S.R.: Data-driven constraint-based motion editing. Ph.D. thesis, École Polytechnique Fédéral de Lausanne (EPFL)—IC School of Computer and Communication Sciences, Lausanne (2009). doi: 10.5075/epfl-thesis-4558. URL
  5. 5.
    Carvalho, S., Boulic, R., Thalmann, D.: Interactive low-dimensional human motion synthesis by combining motion models and pik. Comput. Animat. Virtual Worlds 18 (2007). Special Issue of Computer Animation and Social Agents (CASA2007) Google Scholar
  6. 6.
    Carvalho, S., Boulic, R., Thalmann, D.: Motion pattern preserving ik operating in the motion principal coefficients space. In: Proceedings of 15-th WSCG, pp. 97–104 (2007) Google Scholar
  7. 7.
    Carvalho, S., Boulic, R., Thalmann, D.: Motion pattern encapsulation for data-driven constraint-based motion editing. In: Egges, A., Geraerts, R., Overmars, M. (eds.) Motion in Games. Lecture Notes in Computer Science, vol. 5884, pp. 116–127. Springer, Berlin (2009) CrossRefGoogle Scholar
  8. 8.
    Carvalho, S.R., Vidal, C.A., Boulic, R., Talmann, D.: Propagating latent edited poses across eigen-motions. In: Proceedings of the Computer Graphics International, Ottawa, Canada (2011) Google Scholar
  9. 9.
    Chai, J., Hodgins, J.: Constraint-based motion optimization using a statistical dynamic model. ACM Trans. Graph. 26(3), 8 (2007). doi: CrossRefGoogle Scholar
  10. 10.
    Choi, K., Ko, H.: On-line motion retargetting. J. Vis. Comput. Animat. 11, 223–235 (2000) zbMATHCrossRefGoogle Scholar
  11. 11.
    Glardon, P., Boulic, R., Thalmann, D.: Robust on-line adaptive footplant detection and enforcement for locomotion. Vis. Comput. 22(3), 194–209 (2006). doi: 10.1007/s00371-006-0376-9 CrossRefGoogle Scholar
  12. 12.
    Gleicher, M.: Comparing constraint-based motion editing methods. Graph. Models 63(2), 107–134 (2001) zbMATHCrossRefGoogle Scholar
  13. 13.
    Grassia, F.S.: Practical parameterization of rotations using the exponential map. J. Graph. Tools 3(3), 29–48 (1998) CrossRefGoogle Scholar
  14. 14.
    Grochow, K., Martin, S.L., Hertzmann, A., Popovi, Z.: Style-based inverse kinematics. ACM Trans. Graph. 23(3), 522–531 (2004). doi: CrossRefGoogle Scholar
  15. 15.
    Hanafusa, H., Yoshikawa, T., Nakamura, Y.: Analysis and control of articulated robot with redundancy. In: IFAC, 8th Triennal World Congress, vol. 4, pp. 1927–1932 (1981) Google Scholar
  16. 16.
    Ikemoto, L., Arikan, O., Forsyth, D.: Generalizing motion edits with gaussian processes. ACM Trans. Graph. 28(1), 1–12 (2009). doi: CrossRefGoogle Scholar
  17. 17.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999). doi: CrossRefGoogle Scholar
  18. 18.
    Jolliffe, I.T.: Principal Component Analysis. Springer, Berlin (1986) CrossRefGoogle Scholar
  19. 19.
    Kochanek, D.H.U., Bartels, R.H.: Interpolating splines with local tension, continuity, and bias control. SIGGRAPH Comput. Graph. 18(3), 33–41 (1984). doi: CrossRefGoogle Scholar
  20. 20.
    Kovar, L., Gleicher, M., Pighin, F.: Motion graphs. ACM Trans. Graph. 21(3), 473–482 (2002). doi: CrossRefGoogle Scholar
  21. 21.
    Krüger, B., Tautges, J., Weber, A., Zinke, A.: Fast local and global similarity searches in large motion capture databases. In: Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA’10, pp. 1–10. Eurographics Association, Geneva (2010) Google Scholar
  22. 22.
    Kulpa, R., Multon, F., Arnaldi, B.: Morphology-independent representation of motions for interactive human-like animation. In: EUROGRAPHICS, vol. 24, pp. 343–352 (2005) Google Scholar
  23. 23.
    Lee, J., Shin, S.: A hierarchical approach to interactive motion editing for human-like figures. In: Proceedings of ACM SIGGRAPH, pp. 39–48 (1999) Google Scholar
  24. 24.
    Maciejewski, A.: Dealing with the ill-conditioned equations of motion forarticulated figures. In: Computer Graphics and Applications, vol. 10, pp. 63–71. IEEE Press, New York (1990) Google Scholar
  25. 25.
    Monzani, J.S., Baerlocher, P., Boulic, R., Thalmann, D.: Using an intermediate skeleton and inverse kinematics for motion retargeting. Comput. Graph. Forum 19(3), 11–19 (2000). doi: 10.1111/1467-8659.00393 CrossRefGoogle Scholar
  26. 26.
    Mukai, T., Kuriyama, S.: Geostatistical motion interpolation. ACM Trans. Graph. 24(3), 1062–1070 (2005). doi: CrossRefGoogle Scholar
  27. 27.
    Rasmussen, C.E., Williams, K.I.C.: Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning. MIT Press, Cambridge (2005) Google Scholar
  28. 28.
    Raunhardt, D., Boulic, R.: Motion constraint. Vis. Comput. 25(5–7), 509–518 (2009). doi: 10.1007/s00371-009-0336-2 CrossRefGoogle Scholar
  29. 29.
    Safonova, 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). doi: CrossRefGoogle Scholar
  30. 30.
    Shin, H., Lee, J.: Motion synthesis and editing in low-dimensional spaces. Comput. Animat. Virtual Worlds 17(3–4), 219–227 (2006). doi: 10.1002/cav.v17:3/4 CrossRefGoogle Scholar
  31. 31.
    Shoemake, K.: Animating rotation with quaternion curves. In: SIGGRAPH’85, pp. 245–254. ACM Press, New York (1985). doi: Google Scholar
  32. 32.
    Urtasun, R., Fua, P.: 3d human body tracking using deterministic temporal motion models. In: European Conference on Computer Vision, Prague, Czech Republic (2004) Google Scholar
  33. 33.
    VAL: Visual Agent Laboratory (VAL), Department of Information and Computer Sciences, Toyohashi University of Technology. (2009)
  34. 34.
    van Basten, B., Egges, A.: Flexible splicing of upper-body motion spaces on locomotion. Comput. Graph. Forum 30(7), 1963–1971 (2011) CrossRefGoogle Scholar
  35. 35.
    Whitney, D.E.: Resolved motion rate control of manipulators and human prostheses. In: IEEE Trans. Man-Mach. Syst, vol. 10, pp. 47–53 (1969) Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Schubert R. Carvalho
    • 1
    Email author
  • Ronan Boulic
    • 1
  • Creto A. Vidal
    • 1
  • Daniel Thalmann
    • 1
  1. 1.Laboratory for Computational Sensorimotor NeuroscienceCNSKingstonCanada

Personalised recommendations