Physics Storyboards

Physical simulation and other procedural methods are increasingly popular tools in interactive applications because they generate complex and reactive behaviors given only a few parameter settings. This automation accelerates initial implementation, but also introduces a need to tune the available parameters until the desired behaviors emerge. These adjustments are typically performed iteratively, with the designer repeatedly running—and interacting with—the procedural animation with different parameter settings. Such a process is inaccurate, time consuming, and requires deep understanding and intuition, as parameters often have complex, nonlinear effects.

[1]  Demetri Terzopoulos,et al.  Automated learning of muscle-actuated locomotion through control abstraction , 1995, SIGGRAPH.

[2]  Zoran Popovic,et al.  Motion warping , 1995, SIGGRAPH.

[3]  Ronen Barzel,et al.  A modeling system based on dynamic constraints , 1988, SIGGRAPH.

[4]  Adrien Treuille,et al.  Fluid control using the adjoint method , 2004, ACM Trans. Graph..

[5]  KangKang Yin,et al.  SIMBICON: simple biped locomotion control , 2007, ACM Trans. Graph..

[6]  Nancy S. Pollard,et al.  Efficient synthesis of physically valid human motion , 2003, ACM Trans. Graph..

[7]  Steven M. Seitz,et al.  Interactive manipulation of rigid body simulations , 2000, SIGGRAPH.

[8]  Leonidas J. Guibas,et al.  Exploration of continuous variability in collections of 3D shapes , 2011, ACM Trans. Graph..

[9]  Nikolaus Hansen,et al.  The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.

[10]  David J. Fleet,et al.  Optimizing walking controllers for uncertain inputs and environments , 2010, ACM Trans. Graph..

[11]  Doug L. James,et al.  Many-worlds browsing for control of multibody dynamics , 2007, SIGGRAPH 2007.

[12]  Hans-Christian Hege,et al.  Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration , 2011, IEEE Transactions on Visualization and Computer Graphics.

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

[14]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[15]  Karl Sims,et al.  Evolving virtual creatures , 1994, SIGGRAPH.

[16]  David A. Forsyth,et al.  Sampling plausible solutions to multi-body constraint problems , 2000, SIGGRAPH.

[17]  David C. Brogan,et al.  Animating human athletics , 1995, SIGGRAPH.

[18]  Eugene Fiume,et al.  Limit cycle control and its application to the animation of balancing and walking , 1996, SIGGRAPH.

[19]  N. Mitra,et al.  Exploration of continuous variability in collections of 3D shapes , 2011, SIGGRAPH 2011.

[20]  Arun N. Netravali,et al.  Motion interpolation by optimal control , 1988, SIGGRAPH.

[21]  C. Karen Liu,et al.  Learning physics-based motion style with nonlinear inverse optimization , 2005, ACM Trans. Graph..

[22]  Michael F. Cohen,et al.  Controlling dynamic simulation with kinematic constraints , 1987, SIGGRAPH.

[23]  Daniel Baum,et al.  Perceptually Linear Parameter Variations , 2012, Comput. Graph. Forum.

[24]  Zoran Popovic,et al.  Optimal gait and form for animal locomotion , 2009, ACM Trans. Graph..

[25]  Jovan Popovic,et al.  Style translation for human motion , 2005, ACM Trans. Graph..

[26]  Zicheng Liu,et al.  Hierarchical spacetime control , 1994, SIGGRAPH.

[27]  Jessica K. Hodgins,et al.  Animation of dynamic legged locomotion , 1991, SIGGRAPH.

[28]  Eduard Gröller,et al.  Nodes on Ropes: A Comprehensive Data and Control Flow for Steering Ensemble Simulations , 2011, IEEE Transactions on Visualization and Computer Graphics.

[29]  John F. Hughes,et al.  Plausible motion simulation for computer graphics animation , 1996 .

[30]  Michael F. Cohen,et al.  Interactive spacetime control for animation , 1992, SIGGRAPH.