TY - GEN
T1 - Low-rank based compact representation of motion capture data
AU - Hou, Junhui
AU - Chau, Lap-Pui
AU - He, Ying
AU - Magnenat-Thalmann, Nadia
PY - 2014/1/28
Y1 - 2014/1/28
N2 - In this paper, we propose a practical, elegant and effective scheme for compact mocap data representation. Guided by our analysis of the unique properties of mocap data, the input mocap sequence is optimally segmented into a set of subsequences. Then, we project the subsequences onto a pair of computational orthogonal matrices to explore strong low-rank characteristic within and among the subsequences. The experimental results show that the proposed scheme is much more effective for reducing the data size, compared with the existing techniques.
AB - In this paper, we propose a practical, elegant and effective scheme for compact mocap data representation. Guided by our analysis of the unique properties of mocap data, the input mocap sequence is optimally segmented into a set of subsequences. Then, we project the subsequences onto a pair of computational orthogonal matrices to explore strong low-rank characteristic within and among the subsequences. The experimental results show that the proposed scheme is much more effective for reducing the data size, compared with the existing techniques.
KW - compression
KW - low-rank
KW - Motion capture
UR - http://www.scopus.com/inward/record.url?scp=84946230344&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84946230344&origin=recordpage
U2 - 10.1109/ICIP.2014.7025296
DO - 10.1109/ICIP.2014.7025296
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781479957514
SP - 1480
EP - 1484
BT - 2014 IEEE International Conference on Image Processing, ICIP 2014
PB - IEEE
ER -