Motion capture adaptation for animating characters with varying topology

Abstract

Research on motion retargeting and synthesis for character animation has been mostly focused on the character scale variations. In our recent work we have addressed the motion retargeting problem for characters with different topologies. The purpose of this research project has been to devise 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, altered number of segments and scaled segments. In this thesis, we propose a framework based on the concept of a motion control net (MCN), an external structure analogous to the convex hull of a set of control points defining a parametric curve or a surface patch. Retargeting is achieved as a generalized inverse kinematics problem using an external MCN that encapsulates the motion characteristics of the character embedded in the original motion data and decouples the strong dependency of the motion on the topology of the original motion skeleton. Convergence of the retargeting results requires dynamic modification of the MCN structure. This also allows 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 in the MCN domain with a suitable displacement of control points embedded in the original motion capture sensor data, the method can also generate realistic new motions that resemble the motion patterns in the original data.