Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Turbulent wind fields for gaseous phenomena
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Evaluation of tone mapping operators using a High Dynamic Range display
ACM SIGGRAPH 2005 Papers
A vortex particle method for smoke, water and explosions
ACM SIGGRAPH 2005 Papers
Simulation of smoke based on vortex filament primitives
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Preference learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models
Journal of Global Optimization
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Curl-noise for procedural fluid flow
ACM SIGGRAPH 2007 papers
Preference galleries for material design
ACM SIGGRAPH 2007 posters
Exploratory modeling with collaborative design spaces
ACM SIGGRAPH Asia 2009 papers
Practical bayesian optimization
Practical bayesian optimization
Image-driven navigation of analytical BRDF models
EGSR'06 Proceedings of the 17th Eurographics conference on Rendering Techniques
Active learning of intuitive control knobs for synthesizers using gaussian processes
Proceedings of the 19th international conference on Intelligent User Interfaces
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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".