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".

[1]  Frédo Durand,et al.  Image-driven navigation of analytical BRDF models , 2006, EGSR '06.

[2]  Michael James Sasena,et al.  Flexibility and efficiency enhancements for constrained global design optimization with kriging approximations. , 2002 .

[3]  Robert Bridson,et al.  Curl-noise for procedural fluid flow , 2007, ACM Trans. Graph..

[4]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[5]  Pat Hanrahan,et al.  Exploratory modeling with collaborative design spaces , 2009, SIGGRAPH 2009.

[6]  Nando de Freitas,et al.  Active Preference Learning with Discrete Choice Data , 2007, NIPS.

[7]  Ronald Fedkiw,et al.  A vortex particle method for smoke, water and explosions , 2005, ACM Trans. Graph..

[8]  Nando de Freitas,et al.  Preference galleries for material design , 2007, SIGGRAPH '07.

[9]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[10]  N. Zheng,et al.  Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models , 2006, J. Glob. Optim..

[11]  Jakub Wejchert,et al.  Animation aerodynamics , 1991, SIGGRAPH.

[12]  Wei Chu,et al.  Preference learning with Gaussian processes , 2005, ICML.

[13]  D. Lizotte.  Practical bayesian optimization , 2008 .

[14]  Pat Hanrahan,et al.  Exploratory modeling with collaborative design spaces , 2009, ACM Trans. Graph..

[15]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[16]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[17]  C. D. Perttunen,et al.  Lipschitzian optimization without the Lipschitz constant , 1993 .

[18]  F. Mosteller.  Remarks on the method of paired comparisons: I. The least squares solution assuming equal standard deviations and equal correlations , 1951 .

[19]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[20]  Hiroshi Yamaguchi,et al.  Testing HDR Image Rendering Algorithms , 2004, CIC.

[21]  Eric J. Johnson,et al.  The adaptive decision maker , 1993 .

[22]  Alan Chalmers,et al.  Evaluation of tone mapping operators using a High Dynamic Range display , 2005, ACM Trans. Graph..

[23]  Sonja Kuhnt,et al.  Design and analysis of computer experiments , 2010 .

[24]  Max D. Morris.  The Design and Analysis of Computer Experiments. Thomas J. Santner , Brian J. Williams , and William I. Notz , 2004 .

[25]  Donald R. Jones,et al.  Global versus local search in constrained optimization of computer models , 1998 .

[26]  Paul A. Beardsley,et al.  Design galleries: a general approach to setting parameters for computer graphics and animation , 1997, SIGGRAPH.

[27]  L. Thurstone.  A law of comparative judgment. , 1994 .

[28]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[29]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[30]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[31]  Eugene Fiume,et al.  Turbulent wind fields for gaseous phenomena , 1993, SIGGRAPH.

[32]  Fabrice Neyret,et al.  Simulation of smoke based on vortex filament primitives , 2005, SCA '05.

[33]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.