Abstract
A human walking animation synthesis method is proposed to learn motion discipline from motion capture data and produce new walking animations by using Bayesian Networks. Raw motion capture data is segmented to walking units, and then footprint parameters and keyframes are extracted to train Bayesian Networks. The trained Bayesian Networks can infer appropriate keyframes according to given footprint sequence. The presented method combines the non-deterministic inference ability of Bayesian Networks and control of footprint parameters for end effector. Experiments demonstrate that motions synthesized by our method strictly satisfy space-time constraints and perform naturally.
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Li’an, H., Haiyin, X., Xiongbing, F. (2012). Using Bayesian Networks to Synthesize Human Walking Animation. In: Qian, Z., Cao, L., Su, W., Wang, T., Yang, H. (eds) Recent Advances in Computer Science and Information Engineering. Lecture Notes in Electrical Engineering, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25778-0_3
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DOI: https://doi.org/10.1007/978-3-642-25778-0_3
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