Abstract
Different realistic humanoid motion can be used in vary situations in animation. It also plays an important role in virtual reality. In this paper, we propose a novel method to generate different realistic humanoid motion automatically. Firstly, eigenvectors of a motion sequence is computed using principle component analysis. The principle components are served as “virtual joints” in our system. The number of “virtual joints” can be used to control the realistic level of motions. After given the “virtual joints” number, the actual joints’ parameters of new motion are computed using the selected “virtual joints”. The experiments illuminate that this method has good ability to generate different realistic level motions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Wilkin., A., Kass., H.: Spacetime Constraints. In: Proceedings of Siggraph 1988, pp. 159–68 (1988)
Shin, H.J., Lee., J., Gleicher., M., Shin., S.Y.: Computer puppetry: An importance based aproach. ACM Trans. On Graphics 20(2), 67–94 (2001)
Gleicher., M.: Retargeting motion to new characters. In: Proceedings of Siggraph 1998, pp. 33–42 (1998)
Pullen., K., Bregler., C.: Motion capture assisted animation: Texturing and synthesis. In: Proceedings of Siggraph 2002, pp. 501–508 (2002)
Safonova, A., Hodgins, J.K., Pollard, N.S.: Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. ACM Trans. On Graphics 23(3), 524–521 (2004)
Arikan, O.: Compression of Motion Capture Databases. In: Siggraph 2006 (2006)(to appear)
Popovi ´c., Z., Witkin, A.P.: Physically based motion transformation. In: Proceedings of Siggraph 1999, pp. 11–20 (1999)
Howe., N., Leventon., M., Freeman., W.: Bayesian reconstruction of 3d human motion from single-camera video. In: Advances in Neural Information Processing Systems, vol. 12, pp. 820–826 (1999)
Sindenbladh, H., Black, M.J., Sigal, L.: Implicit probabilistic models of human motion for synthesis and tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 784–800. Springer, Heidelberg (2002)
Jenkins, O.C., Mataric, M.J.: Automated derivation of behavior vocabularies for autonomous humanoid motion. In: AAMAS 2003: Proceedings of the second international joint conference on Autonomous agents and multiagent systems, pp. 225–232 (2003)
Scholkopf, B., Smola, A.J., Muller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5), 1299–1319 (1998)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Z., Deng, Y., Li, H. (2006). Generating Different Realistic Humanoid Motion. In: Pan, Z., Cheok, A., Haller, M., Lau, R.W.H., Saito, H., Liang, R. (eds) Advances in Artificial Reality and Tele-Existence. ICAT 2006. Lecture Notes in Computer Science, vol 4282. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941354_9
Download citation
DOI: https://doi.org/10.1007/11941354_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49776-9
Online ISBN: 978-3-540-49779-0
eBook Packages: Computer ScienceComputer Science (R0)