Computational Visual Media

, Volume 3, Issue 1, pp 49–60 | Cite as

Dynamic skin deformation simulation using musculoskeletal model and soft tissue dynamics

  • Akihiko Murai
  • Q. Youn Hong
  • Katsu Yamane
  • Jessica K. Hodgins
Open Access
Research Article


Deformation of skin and muscle is essential for bringing an animated character to life. This deformation is difficult to animate in a realistic fashion using traditional techniques because of the subtlety of the skin deformations that must move appropriately for the character design. In this paper, we present an algorithm that generates natural, dynamic, and detailed skin deformation (movement and jiggle) from joint angle data sequences. The algorithm has two steps: identification of parameters for a quasi-static muscle deformation model, and simulation of skin deformation. In the identification step, we identify the model parameters using a musculoskeletal model and a short sequence of skin deformation data captured via a dense marker set. The simulation step first uses the quasi-static muscle deformation model to obtain the quasi-static muscle shape at each frame of the given motion sequence (slow jump). Dynamic skin deformation is then computed by simulating the passive muscle and soft tissue dynamics modeled as a mass–spring–damper system. Having obtained the model parameters, we can simulate dynamic skin deformations for subjects with similar body types from new motion data. We demonstrate our method by creating skin deformations for muscle co-contraction and external impacts from four different behaviors captured as skeletal motion capture data. Experimental results show that the simulated skin deformations are quantitatively and qualitatively similar to measured actual skin deformations.


three-dimensional graphics and realism musculoskeletal model quasi-static muscle model dynamic skin deformation 

Supplementary material

41095_2016_65_MOESM1_ESM.mp4 (167.2 mb)
Supplementary material, approximately 167 MB.


  1. [1]
    Autodesk. Maya 2015. 2015.Google Scholar
  2. [2]
    Lewis, J. P.; Anjyo, K.; Rhee, T.; Zhang, M.; Pighin, F.; Deng, Z. Practice and theory of blendshape facial models. In: Proceedings of Eurographics 2014—State of the Art Reports, 2014.Google Scholar
  3. [3]
    Pons-Moll, G.; Romero, J.; Mahmood, N.; Black, M. J. Dyna: A model of dynamic human shape in motion. ACM Transactions on Graphics Vol. 34, No. 4, Article No. 120, 2015.Google Scholar
  4. [4]
    Si, W.; Lee, S.-H.; Sifakis, E.; Terzopoulos, D. Realistic biomechanical simulation and control of human swimming. ACM Transactions on Graphics Vol. 34, No. 1, Article No. 10, 2014.Google Scholar
  5. [5]
    Murai, A.; Takeichi, K.; Miyatake, T.; Nakamura, Y. Musculoskeletal modeling and physiological validation. In: Proceedings of 2014 IEEE International Workshop on Advanced Robotics and its Social Impacts, 108–113, 2014.CrossRefGoogle Scholar
  6. [6]
    Park, S. I.; Hodgins, J. K. Capturing and animating skin deformation in human motion. ACM Transactions on Graphics Vol. 25, No. 3, 881–889, 2006.CrossRefGoogle Scholar
  7. [7]
    Vaillant, R.; Barthe, L.; Guennebaud, G.; Cani, M.-P.; Rohmer, D.; Wyvill, B.; Gourmel, O.; Paulin, M. Implicit skinning: Real-time skin deformation with contact modeling. ACM Transactions on Graphics Vol. 32, No. 4, Article No. 125, 2013.Google Scholar
  8. [8]
    Chadwick, J. E.; Haumann, D. R.; Parent, R. E. Layered construction for deformable animated characters. In: Proceedings of the 16th Annual Conference on Computer Graphics and Interactive Techniques, 243–252, 1989.Google Scholar
  9. [9]
    Tsoli, A.; Mahmood, N.; Black, M. J. Breathing life into shape: Capturing, modeling and animating 3D human breathing. ACM Transactions on Graphics Vol. 33, No. 4, Article No. 52, 2014.Google Scholar
  10. [10]
    Sueda, S.; Pai, D. K. Dynamic simulation of the hand. In: The Human Hand as an Inspiration for Robot Hand Development. Balasubramanian, R.; Santos, V. J. Eds. Springer International Publishing, 267–288, 2014.CrossRefGoogle Scholar
  11. [11]
    Lee, S.-H.; Sifakis, E.; Terzopoulos, D. Comprehensive biomechanical modeling and simulation of the upper body. ACM Transactions on Graphics Vol. 28, No. 4, Article No. 99, 2009.Google Scholar
  12. [12]
    Pratscher, M.; Coleman, P.; Laszlo, J.; Singh, K. Outside-in anatomy based character rigging. In: Proceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 329–338, 2005.CrossRefGoogle Scholar
  13. [13]
    Mitsuhashi, N.; Fujieda, K.; Tamura, T.; Kawamoto, S.; Takagi, T.; Okubo, K. BodyParts3D: 3D structure database for anatomical concepts. Nucleic Acids Research Vol. 37, D782–D785, 2009.CrossRefGoogle Scholar
  14. [14]
    Zordan, V. B.; Celly, B.; Chiu, B.; DiLorenzo, P. C. Breathe easy: Model and control of simulated respiration for animation. In: Proceedings of the 2004 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 29–37, 2004.CrossRefGoogle Scholar
  15. [15]
    Koch, R. M.; Gross, M. H.; Carlsy, F. R.; von Büren, D. F.; Fankhauser, G.; Parish, Y. I. H. Simulating facial surgery using finite element models. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, 421–428, 1996.Google Scholar
  16. [16]
    Blemker, S.; Teran, J.; Sifakis, E.; Fedkiw, R.; Delp, S. Fast 3D muscle simulations using a new quasistatic invertible finite-element algorithm. In: Proceedings of International Symposium on Computer Simulation in Biomechanics, 2005.Google Scholar
  17. [17]
    Webb, J. D.; Blemker, S. S.; Delp, S. L. 3D finite element models of shoulder muscles for computing lines of actions and moment arms. Computer Methods in Biomechanics and Biomedical Engineering Vol. 17, No. 8, 829–837, 2014.CrossRefGoogle Scholar
  18. [18]
    Fan, Y.; Litven, J.; Pai, D. K. Active volumetric musculoskeletal systems. ACM Transactions on Graphics Vol. 33, No. 4, Article No. 152, 2014.Google Scholar
  19. [19]
    Anguelov, D.; Srinivasan, P.; Koller, D.; Thrun, S.; Rodgers, J.; Davis, J. SCAPE: Shape completion and animation of people. ACM Transactions on Graphics Vol. 24, No. 3, 408–416, 2005.CrossRefGoogle Scholar
  20. [20]
    Park, S. I.; Hodgins, J. K. Data-driven modeling of skin and muscle deformation. ACM Transactions on Graphics Vol. 27, No. 3, Article No. 96, 2008.Google Scholar
  21. [21]
    Wang, H.; Hecht, F.; Ramamoorthi, R.; O’Brien, J. Example-based wrinkle synthesis for clothing animation. ACM Transactions on Graphics Vol. 29, No. 4, Article No. 107, 2010.Google Scholar
  22. [22]
    Delp, S. L.; Anderson, F. C.; Arnold, A. S.; Loan, P.; Habib, A.; John, C. T.; Guendelman, E.; Thelen, D. G. OpenSim: Open-source software to create and analyze dynamic simulations of movement. IEEE Transactions on Biomedical Engineering Vol. 54, No. 11, 1940–1950, 2007.CrossRefGoogle Scholar
  23. [23]
    Nakamura, Y.; Yamane, K.; Fujita, Y.; Suzki, I. Somatosensory computation for manmachine interface from motion-capture data and musculoskeletal human model. IEEE Transactions on Robotics Vol. 21, No. 1, 58–66, 2005.CrossRefGoogle Scholar
  24. [24]
    AIST. Japanese body dimension data 1997–98. 1998. Available at index.html.Google Scholar
  25. [25]
    Yamane, K.; Nakamura, Y. Natural motion animation through constraining and deconstraining at will. IEEE Transactions on Visualization and Computer Graphics Vol. 9, No. 3, 352–360, 2003.CrossRefGoogle Scholar
  26. [26]
    Luh, J. Y. S.; Walke, M. W.; Paul, R. P. C. On-line computational scheme for mechanical manipulators. Journal of Dynamic Systems, Measurement, and Control Vol. 102, No. 2, 69–76, 1980.MathSciNetCrossRefGoogle Scholar
  27. [27]
    Nakamura, Y.; Yamane, K.; Murai, A. Macroscopic modeling and identification of the human neuromuscular network. In: Proceedings of the 28th Annual International Conference of the IEEE, 99–105, 2006.Google Scholar
  28. [28]
    Yamane, K.; Fujita, Y.; Nakamura, Y. Estimation of physically and physiologically valid somatosensory information. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2624–2630, 2005.CrossRefGoogle Scholar
  29. [29]
    Coleman, T. F.; Li, Y. On the convergence of interior reflective Newton methods for nonlinear minimization subject to bounds. Mathematical Programming Vol. 67, No. 1, 189–224, 1994.MathSciNetCrossRefzbMATHGoogle Scholar
  30. [30]
    Kardel, T. Niels Stensen’s geometrical theory of muscle contraction (1667): A reappraisal. Journal of Biomechanics Vol. 23, No. 10, 953–965, 1990.CrossRefGoogle Scholar
  31. [31]
    Howell, J.; Chleboun, G.; Conatser, R. Muscle stiffness, strength loss, swelling and soreness following exercise-induced injury in humans. The Journal of Physiology Vol. 464, No. 1, 183–196, 1993.CrossRefGoogle Scholar
  32. [32]
    Swope, W. C.; Andersen, H. C.; Berens, P. H.; Wilson, K. R. A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: Application to small water clusters. The Journal of Chemical Physics Vol. 76, 637–649, 1982.CrossRefGoogle Scholar
  33. [33]
    Baraff, D.; Witkin, A. Large steps in cloth simulation. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, 43–54, 1998.Google Scholar

Copyright information

© The Author(s) 2016

Open Access The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Other papers from this open access journal are available free of charge from To submit a manuscript, please go to

Authors and Affiliations

  • Akihiko Murai
    • 1
  • Q. Youn Hong
    • 2
  • Katsu Yamane
    • 3
  • Jessica K. Hodgins
    • 3
  1. 1.National Institute of Advanced Industrial Science and TechnologyTokyoJapan
  2. 2.Carnegie Mellon UniversityPittsburghUSA
  3. 3.Disney ResearchDisneyUSA

Personalised recommendations