“Heads up!: biomechanical modeling and neuromuscular control of the neck” by Lee and Terzopoulos

Unlike the human face, the neck has been largely overlooked in the computer graphics literature, this despite its complex anatomical structure and the important role that it plays in supporting the head in balance while generating the controlled head movements that are essential to so many aspects of human behavior. This paper makes two major contributions. First, we introduce a biomechanical model of the human head-neck system. Emulating the relevant anatomy, our model is characterized by appropriate kinematic redundancy (7 cervical vertebrae coupled by 3-DOF joints) and muscle actuator redundancy (72 neck muscles arranged in 3 muscle layers). This anatomically consistent biomechanical model confronts us with a challenging motor control problem, even for the relatively simple task of balancing the mass of the head in gravity atop the cervical spine. Hence, our second contribution is a novel neuromuscular control model for human head animation that emulates the relevant biological motor control mechanisms. Incorporating low-level reflex and high-level voluntary sub-controllers, our hierarchical controller provides input motor signals to the numerous muscle actuators. In addition to head pose and movement, it controls the tone of mutually opposed neck muscles to regulate the stiffness of the head-neck multibody system. Employing machine learning techniques, the neural networks within our neuromuscular controller are trained offline to efficiently generate the online pose and tone control signals necessary to synthesize a variety of autonomous movements for the behavioral animation of the human head and face.

1. Albrecht, I., Haber, J., and Seidel, H.-P. 2003. Construction and animation of anatomically based human hand models. In ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA’03), 98–109. Google ScholarDigital Library
2. Anderson, F., and Pandy, M. 2001. Static and dynamic optimization solutions for gait are practically equivalent. Journal of Biomechanics 34, 153–161.Google ScholarCross Ref
3. Carpenter, R. 1988. Movements of the Eyes, 2nd ed. Pion, London.Google Scholar
4. Chen, D. T., and Zeltzer, D. 1992. Pump it up: Computer animation of a biomechanically based model of muscle using the finite element method. In Computer Graphics (Proceedings of ACM SIGGRAPH 92), vol. 26, 89–98. Google ScholarDigital Library
5. Delp, S., and Loan, J. 1995. A software system to develop and analyze models of musculoskeletal structures. Computers in Biology and Medicine 25, 21–34.Google ScholarCross Ref
6. Faloutsos, P., van de Panne, M., and Terzopoulos, D. 2001. Composable controllers for physics-based character animation. In Proceedings of ACM SIGGRAPH 2001, Computer Graphics Proceedings, Annual Conference Series, 251–260. Google ScholarDigital Library
7. Grzeszczuk, R., and Terzopoulos, D. 1995. Automated learning of muscle-actuated locomotion through control abstraction. In Proceedings of ACM SIGGRAPH 95, Computer Graphics Proceedings, Annual Conference Series, 63–70. Google ScholarDigital Library
8. Grzeszczuk, R., Terzopoulos, D., and Hinton, G. 1998. Neuroanimator: Fast neural network emulation and control of physics-based models. In Proc. of ACM SIGGRAPH 98, Computer Graphics Proceedings, Annual Conference Series, 9–20. Google ScholarDigital Library
9. Hay, J., and Reid, J. 1988. Anatomy, Mechanics, and Human Motion, 2nd ed. Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
10. Hodgins, J. K., Wooten, W. L., Brogan, D. C., and O’Brien, J. F. 1995. Animating human athletics. In Proceedings of ACM SIGGRAPH 95, Computer Graphics Proceedings, Annual Conference Series, 71–78. Google ScholarDigital Library
11. Hogan, N. 1984. Adaptive control of mechanical impedance by coactivation of antagonist muscles. IEEE Transactions on Automatic Control AC-29 (Aug.), 681–690.Google ScholarCross Ref
12. Irving, G., Teran, J., and Fedkiw, R. 2004. Invertible finite elements for robust simulation of large deformation. In ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA’04), 131–140. Google ScholarDigital Library
13. Kähler, K., Haber, J., and Seidel, H.-P. 2001. Geometry-based muscle modeling for facial animation. In Graphics Interface 2001, 37–46. Google ScholarDigital Library
14. Kandel, E., Schwartz, J., and Jessell, T. 2000. Principles of Neural Science, 4th ed. McGraw Hill, New York.Google Scholar
15. Kapandji, I. 1974. The Physiology of the Joints. Vol. 3: The Trunk and the Vertebral Column. Churchill Livingstone, Edinburgh.Google Scholar
16. Kawato, M., Furukawa, K., and Suzuki, R. 1987. A hierarchical neural network model for control and learning of voluntary movement. Biological Cybernetics 57, 169–185.Google ScholarCross Ref
17. Keshner, E., and Peterson, B. 1995. Mechanisms controlling human head stabilization. I. Head-neck dynamics during random rotations in the horizontal plane. Journal of Neurophysiology 73, 2293–2301.Google ScholarCross Ref
18. Kim, J., and Hemami, H. 1998. Coordinated three-dimensional motion of the head and torso by dynamic neural networks. IEEE Trans. on Systems, Man and Cybernetics. B 5, 653–666. Google ScholarDigital Library
19. Komura, T., Shinagawa, Y., and Kunii, T. L. 1997. A muscle-based feed-forward controller of the human body. Computer Graphics Forum 16, 3 (Aug.), 165–176.Google ScholarCross Ref
20. Komura, T., Shinagawa, Y., and Kunii, T. L. 2000. Creating and retargeting motion by the musculoskeletal human body model. The Visual Computer 16, 5, 254–270.Google ScholarCross Ref
21. Lee, Y., Terzopoulos, D., and Waters, K. 1995. Realistic modeling for facial animation. In Proceedings of ACM SIGGRAPH 95, Computer Graphics Proceedings, Annual Conference Series, 55–62. Google ScholarDigital Library
22. Monheit, G., and Badler, N. I. 1991. A kinematic model of the human spine and torso. IEEE Computer Graphics & Applications 11, 2 (Mar.), 29–38. Google ScholarDigital Library
23. Neff, M., and Fiume, E. 2002. Modeling tension and relaxation for computer animation. In ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA’02), 81–88. Google ScholarDigital Library
24. Ng-Thow-Hing, V. 2001. Anatomically-Based Models for Physical and Geometrical Reconstruction of Humans and Other Animals. PhD thesis, University of Toronto, Department of Computer Science. Google ScholarDigital Library
25. Pai, D. K., Sueda, S., and Wei, Q. 2005. Fast physically based musculoskeletal simulation. In Proceedings of Sketches & Applications of ACM SIGGRAPH 2005. Google ScholarDigital Library
26. Scheepers, F., Parent, R. E., Carlson, W. E., and May, S. F. 1997. Anatomy-based modeling of the human musculature. In Proceedings of ACM SIGGRAPH 97, Computer Graphics Proceedings, Annual Conference Series, 163–172. Google ScholarDigital Library
27. Sifakis, E., Neverov, I., and Fedkiw, R. 2005. Automatic determination of facial muscle activations from sparse motion capture marker data. ACM Transactions on Graphics 24, 3 (Aug.), 417–425. Proceedings of ACM SIGGRAPH 2005. Google ScholarDigital Library
28. Spellucci, P. Donlp2. www.netlib.org/ampl/solvers/donlp2/.Google Scholar
29. Terzopoulos, D., and Lee, Y. 2004. Behavioral animation of faces. In Facial Modeling and Animation, J. Haber and D. Terzopoulos, Eds., vol. 60 of ACM SIGGRAPH 2004 Course Notes. ACM SIGGRAPH, Aug., 119–128.Google Scholar
30. Tsang, W., Singh, K., and Fiume, E. 2005. Helping hand: An anatomically accurate inverse dynamics solution for unconstrained hand motion. In ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA’05), 319–328. Google ScholarDigital Library
31. Tu, X., and Terzopoulos, D. 1994. Artificial fishes: Physics, locomotion, perception, behavior. In Proceedings of ACM SIGGRAPH 94, Computer Graphics Proceedings, Annual Conference Series, 43–50. Google ScholarDigital Library
32. Vasavada, A., Li, S., and Delp, S. 1998. Influence of muscle morphometry and moment arms on the moment-generating capacity of human neck muscles. Spine 23, 412–422.Google ScholarCross Ref
33. Warfel, J. 1985. The Head, Neck, and Trunk, 5 ed. Lea & Febiger, Philadelphia.Google Scholar
34. Wilhelms, J., and Gelder, A. V. 1997. Anatomically based modeling. In Proceedings of ACM SIGGRAPH 97, Computer Graphics Proceedings, Annual Conference Series, 173–180. Google ScholarDigital Library
35. Winters, J., and Crago, P., Eds. 2000. Biomechanics and Neural Control of Posture and Movement. Springer-Verlag, New York.Google Scholar
36. Yamazaki, Y., Ohkuwa, T., Itoh, H., and Suzuki, M. 1994. Reciprocal activation and coactivation in antagonistic muscles during rapid goal-directed movements. Brain Research Bulletin 34, 587–593.Google ScholarCross Ref
37. Yin, K., Cline, M., and Pai, D. K. 2003. Motion perturbation based on simple neuromotor control models. In Proceedings of the 11th Pacific Conference on Computer Graphics and Applications (PG’03), IEEE Computer Society. Google ScholarDigital Library
38. Zordan, V. B., Celly, B., Chiu, B., and Dilorenzo, P. C. 2004. Breathe easy: Model and control of simulated respiration for animation. In ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA’04), 29–37. Google ScholarDigital Library