The authors have been studying motion database systems. When entering an example motion as the query for the similarity search of motion data, it is natural to enter it as a semantic primitive motion, i.e., "walk", "jump", "run" and so on. Mostly, one motion data consists of several primitive motions. It is necessary to divide a composite motion into primitive motions. There are no algorithms able to automatically divide a composite motion into semantic primitive motions perfectly because the semantic meanings of primitive motions are strongly depending upon the human senses. A curve simplification algorithm is used for the key-posture extraction from motion data. This helps us to divide a composite motion into its primitive motions. The key-posture extraction is also used for the motion compression. In this paper, the authors propose a new efficient key-posture extraction method that hierarchically applies the curve simplification algorithm to the feature joints of a human figure model
[1]
Lance Williams,et al.
Motion signal processing
,
1995,
SIGGRAPH.
[2]
Daniel Thalmann,et al.
Key-posture extraction out of human motion data
,
2001,
2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[3]
Ken-ichi Anjyo,et al.
Fourier principles for emotion-based human figure animation
,
1995,
SIGGRAPH.
[4]
Jessica K. Hodgins,et al.
Adapting simulated behaviors for new characters
,
1997,
SIGGRAPH.
[5]
Kuniaki Uehara,et al.
A Motion Recognition Method by Using Primitive Motions
,
2000,
VDB.