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Can I read Constraint-based Motion Optimization Using a Statistical Dynamic Model on EtoBox?
Constraint-based Motion Optimization Using a Statistical Dynamic Model by Jinxiang Chai; Jessica K. Hodgins is a Computer Science article available to read on EtoBox.
What is Constraint-based Motion Optimization Using a Statistical Dynamic Model about?
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.
Who reads Constraint-based Motion Optimization Using a Statistical Dynamic Model?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Jinxiang Chai; Jessica K. Hodgins
- Publisher
- ACM
- Published
- 2007
- Language
- EN
- Field
- Computer Science (Physical Sciences)