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Constraint-based motion optimization using a statistical dynamic model
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AI
DOI：10.1145/1239451.1239459.png)
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摘要
En 中文
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.
Keyword:
human body animation
facial animation
motion control
statistical dynamic models
spatial-temporal constraints
constraint-based motion synthesis
motion capture data
