Abstract
This paper presents a novel human action model based on key-frames which is suitable for animation purposes. By defining an action as a sequence of time-ordered body posture configurations, we consider that the most characteristic postures (called key-frames) are enough for modeling such an action. As characteristic postures are found to correspond to low likelihood values, we build a human action eigenspace, called aSpace, which is used to estimate the likelihood value for each posture. Once the key-frames have been found automatically, they are used to build a human action model called p–action by means of interpolation between key-frames. This parameterized model represents the time evolution of the human body posture during a prototypical action, and it can be used for computer animation. As a result, realistic and smooth motion is achieved. Furthermore, realistic virtual sequences involving several actions can be automatically generated.
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
Akita, K.: Image sequence analysis of real world human motion. Pattern Recognition 17(1), 73–83 (1984)
Ben-Aire, J., Wang, Z., Pandit, P., Rajaram, S.: Human activity recognition using multidimensional indexing. IEEE Trans. Pattern Analysis and Machine Intelligence 24(8), 1091–1104 (2002)
Borotschnig, H., Paletta, L., Prantl, M., Pinz, A.: Appearance-based active object recognition. Image and Vision Computing 18, 715–727 (2000)
Cheng, J., Moura, M.F.: Capture and represention of human walking in live video sequences. IEEE Transactions on Multimedia 1(2), 144–156 (1999)
Gonzàlez, J., Varona, X., Roca, F.X., Villanueva, J.J.: aSpaces: Action spaces for recognition and synthesis of human actions. In: Perales, F.J., Hancock, E.R. (eds.) AMDO 2002. LNCS, vol. 2492, pp. 189–200. Springer, Heidelberg (2002)
Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 696–710 (1997)
Parent, R.: Computer Animation. Algorithms and Techniques. Morgan Kaufmann Publishers, San Francisco (2002)
Press, W., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C. Cambridge University Press, Cambridge (1988)
Sullivan, J., Carlsson, S.: Recognizing and tracking human action. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 629–644. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gonzàlez, J., Varona, J., Roca, F.X., Villanueva, J.J. (2003). Automatic Keyframing of Human Actions for Computer Animation. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-540-44871-6_34
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40217-6
Online ISBN: 978-3-540-44871-6
eBook Packages: Springer Book Archive