Paper:
Language: English:

Behavior Learning and Animation Synthesis of Falling Flat Objects

Kohta Aoki^, Osamu Hasegawa^,, and Hiroshi Nagahashi^**

Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8503, Japan
PRESTO, Japan Science and Technology Corp. (JST)

Received: August 11, 2003

Accepted: December 1, 2003

Keywords: pattern learning, computer animation, natural phenomena, self-organizing map

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.8, No.2 pp. 223-230, 2004

Abstract

In this paper, we describe an approach to learning patterns from sample data sequences and generating new data sequences through learned models. The target application of this work is the animation of natural phenomena, especially falling behavior of flat objects. The natural object or phenomenon to be animated is recorded using one camera, and its characteristic behavior is captured. Feature vectors are defined as the representation of behavior and are automatically extracted from captured videos. By learning the structure of a set of sample vector sequences, the learned model can generate a novel pattern through the underlying structure. These generated patterns could differ from every original vector sequence but preserve characteristics of subject behavior. We can use such patterns to synthesize natural-looking animation.

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