A Bayesian interactive optimization approach to procedural animation design

The computer graphics and animation fields are filled with applications that require the setting of tricky parameters. In many cases, the models are complex and the parameters unintuitive for non-experts. In this paper, we present an optimization method for setting parameters of a procedural fluid animation system by showing the user examples of different parametrized animations and asking for feedback. Our method employs the Bayesian technique of bringing in “prior ” belief based on previous runs of the system and/or expert knowledge, to assist users in finding good parameter settings in as few steps as possible. To do this, we introduce novel extensions to Bayesian optimization, which permit effective learning for parameter-based procedural animation applications. We show that even when users are trying to find a variety of different target animations, the system can learn and improve. We demonstrate the effectiveness of our method compared to related active learning methods. We also present a working application for assisting animators in the challenging task of designing curl-based velocity fields, even with minimal domain knowledge other than identifying when a simulation “looks right”.