Interactive Bayesian Optimization for Animation

By Eric Brochu

The computer graphics field is filled with applications that require the setting of tricky parameters, which are often complex and unintuitive for non-experts.  We present an active learning method and application for setting parameters for a procedural fluid animation system.  In this system, we learn a model by showing the user a gallery of instances of different parametrized animations and asking for preference feedback.  Our method employs Bayesian optimization to incorporate prior knowledge based on previous sessions and/or expert knowledge to assist users in finding good parameter settings in as few steps as possible.

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