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.
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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)
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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
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DOI: https://doi.org/10.1007/978-3-540-44871-6_34
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