constraint-based motion optimization using a statistical dynamic model
In this paper, we present a technique for generating animation from a variety of user-defined constraints. We pose constraint-based motion synthesis as a maximum a posterior (MAP) problem and develop an optimization framework that generates natural motion satisfying user constraints. The system automatically learns a statistical dynamic model from motion capture data and then enforces it as a motion prior. This motion prior, together with user-defined constraints, comprises a trajectory optimization problem. Solving this problem in the low-dimensional space yields optimal natural motion that achieves the goals specified by the user. We demonstrate the effectiveness of this approach by generating whole-body and facial motion from a variety of spatial-temporal constraints.
statistical dynamic models constraint-based motion synthesis motion capture data spatial-temporal constraints facial animation human body animation motion control