TY - GEN
T1 - Automatic foot-plant constraints detection shoes
AU - Gao, Yan
AU - Ma, Lizhuang
AU - Wu, Xiaomao
AU - Chen, Zhihua
PY - 2005
Y1 - 2005
N2 - Motion editing methods have been widely used to create realistic human animation, which is vital to maintain footplant constrains during motion editing process. In this paper, we present a simple but efficient way for footplant constraints detection. Existing methods for automatic footplant detection are all software-based which are sensitive to motion data noises. We improve this work by hardware instead. We equip the actor with a simple pressure trigger circuit under his shoes. Whenever the toes or heels of the actor touch the ground, the circuit will be switched on and this information will be captured by motion capture system. Combining this additional information with the ordinary captured motion data, we can identify footplant constraints effectively. Such additional information will then be saved into the output motion capture file and can be used conveniently by the user. The new framework we developed can provide more precise detection results than previous software-based methods. Moreover, by pre-computing the footplant constraints, the time for footplant constraints detection in previous software-based methods can be saved.
AB - Motion editing methods have been widely used to create realistic human animation, which is vital to maintain footplant constrains during motion editing process. In this paper, we present a simple but efficient way for footplant constraints detection. Existing methods for automatic footplant detection are all software-based which are sensitive to motion data noises. We improve this work by hardware instead. We equip the actor with a simple pressure trigger circuit under his shoes. Whenever the toes or heels of the actor touch the ground, the circuit will be switched on and this information will be captured by motion capture system. Combining this additional information with the ordinary captured motion data, we can identify footplant constraints effectively. Such additional information will then be saved into the output motion capture file and can be used conveniently by the user. The new framework we developed can provide more precise detection results than previous software-based methods. Moreover, by pre-computing the footplant constraints, the time for footplant constraints detection in previous software-based methods can be saved.
UR - https://www.scopus.com/pages/publications/33748858706
U2 - 10.1109/CGIV.2005.23
DO - 10.1109/CGIV.2005.23
M3 - 会议稿件
AN - SCOPUS:33748858706
SN - 0769523927
SN - 9780769523927
T3 - Proceedings of the Conference on Computer Graphics, Imaging and Vision: New Trends 2005
SP - 87
EP - 92
BT - Proceedings of the Conference on Computer Graphics, Imaging and Vision
T2 - 2nd Conference on Computer Graphics, Imaging, and Vision: New Trends 2005
Y2 - 26 July 2005 through 29 July 2005
ER -