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Motion retargeting in the presence of topological variations
Author(s) -
Baciu George,
Iu Bartholomew K. C.
Publication year - 2006
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.72
Subject(s) - retargeting , computer science , character animation , inverse kinematics , computer vision , motion capture , motion (physics) , kinematics , topology (electrical circuits) , animation , artificial intelligence , character (mathematics) , computer graphics (images) , computer animation , algorithm , mathematics , robot , geometry , physics , classical mechanics , combinatorics
Research on motion retargeting and synthesis for character animation has been mostly focused on character scale variations. In our recent work we have addressed the motion retargeting problem for characters with slightly different topologies. In this paper we present a new method for retargeting captured motion data to an enhanced character skeleton having a topology that is different from that of the original captured motion. The new topology could include altered hierarchical structures and scaled segments. In order to solve this problem, we propose a framework based on the concept of a motion control net (MCN). This is an external structure analogous to the convex hull of a set of control points defining a parametric curve or a surface patch. The MCN encapsulates the motion characteristics of the character. Retargeting is achieved as a generalized inverse kinematics problem using an external MCN. The retargeting solution requires the dynamic modification of the MCN structure. This also allows US to interactively edit the MCN and modify the conditions for the motion analysis. The new method can automatically synthesize new segment information and, by combining the segment motion into the MCN domain with a suitable displacement of control points embedded in the original motion capture sensor data, it can also generate realistic new motions that resemble the motion patterns in the original data. Copyright © 2006 John Wiley & Sons, Ltd.