Constraint-based Motion Optimization Using A Statistical Dynamic Model
|Jinxiang Chai||Jessica K. Hodgins|
ACM Transactions on Graphics (SIGGRAPH 2007) (2007)
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 in two domains: human body animation and facial animation. We show that the system can generate natural-looking animation from key-frame constraints, key-trajectory constraints, and a combination of these two constraints. For example, the user can generate a walking animation from a small set of key frames and foot contact constraints. The user can also specify a small set of key trajectories for the root, hands and feet positions to generate a realistic jumping motion. The system can generate motions for a character whose skeletal model is markedly different from those of the subjects in the database. We also show that the system can use a statistical dynamic model learned from a normal walking sequence to create new motion such as walking on a slope.
Jinxiang Chai, Jessica K. Hodgins (2007). Constraint-based Motion Optimization Using A Statistical Dynamic Model. ACM Transactions on Graphics (SIGGRAPH 2007), 26(3).
author = "Jinxiang Chai and Jessica K. Hodgins",
title = "Constraint-based Motion Optimization Using A Statistical Dynamic Model",
year = "2007",
month = aug,
journal = "ACM Transactions on Graphics (SIGGRAPH 2007)",
volume = "26",
number = "3",