Physical simulation of dynamic objects has become commonplace in computer graphics because it produces highly realistic animations. In this paradigm the animator provides few physical parameters such as the objects' initial positions and velocities, and the simulator automatically generates realistic motions. The resulting motion, however, is difficult to control because even a small adjustment of the input parameters can drastically affect the subsequent motion. Furthermore, the animator often wishes to change the endresult of the motion instead of the initial physical parameters. We describe

Traditional collision intensive multi-body simulations are difficult to control due to extreme sensitivity to initial conditions or model parameters. Furthermore, there may be multiple ways to achieve any one goal, and it may be difficult to codify a user's preferences before they have seen the available solutions. In this paper we extend simulation models to include plausible sources of uncertainty, and then use a Markov chain Monte Carlo algorithm to sample multiple animations that satisfy constraints. A user can choose the animation they prefer, or applications can take direct advantage of the multiple solutions. Our technique is applicable when a probability can be attached to each animation, with "good" animations having high probability, and for such cases we provide a definition of physical plausibility for animations. We demonstrate our approach with examples of multi-body rigid-body simulations that satisfy constraints of various kinds, for each case presenting animations that are true to a physical model, are significantly different from each other, and yet still satisfy the constraints. CR Descriptors: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism - Animation; I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling - Physically based modeling; I.6.5 [Simulation and Modeling]: Model Development - Modeling methodologies G.3 [Probability and Statistics]: Probabilistic algorithms; Keywords: plausible motion, Markov chain Monte Carlo, motion synthesis, spacetime constraints 1

Accuracy is the ubiquitous goal of dynamic simulation, in order to yield the “correct ” motion. But for creating animation, what is really of interest is “plausible ” motion, which is somewhat different. We discuss what we mean by plausible simulation, how it differs from “accurate ” simulation, and why we think it’s a worthwhile area to study. The discussion touches on questions of physically plausible vs. visually plausible motion, plausible simulation in a noisy or textured environment, and probability measures for motion, as well as issues for forward and inverse problems. 1

Character animations produced with motion capture data have many of the stylistic details seen in human motion while those generated with simulation are physically realistic for the dynamic parameters of the character. We combine these two approachesby tracking and modifying human motion capture data using dynamic simulation and constraints. The tracking system generates motion that is appropriate for the graphical character while maintaining characteristics of the original human motion. The system imposes contact and task constraints to add dynamic impacts for interactions with the environment and to modify motions at the behavior level. The system is able to edit motion data to account for changes in the character and the environment as well as create smooth transitions between motion capture sequences. We demonstrate the power of combining these two approaches by tracking data for a variety of upper-body motions and by animating models with differing kinematic and dyna...

Animation techniques for controlling passive simulation are commonly based on an optimization paradigm: the user provides goals a priori, and sophisticated numerical methods minimize a cost function that represents these goals. Unfortunately, for multibody systems with discontinuous contact events these optimization problems can be highly nontrivial to solve, and many-hour offline optimizations, unintuitive parameters, and convergence failures can frustrate end-users and limit usage. On the other hand, users are quite adaptable, and systems which provide interactive feedback via an intuitive interface can leverage the user’s own abilities to quickly produce interesting animations. However, the online computation necessary for interactivity limits scene complexity in practice. We introduce Many-Worlds Browsing, a method which circumvents these limits by exploiting the speed of multibody simulators to compute numerous example simulations in parallel (offline and online), and allow the user to browse and modify them interactively. We demonstrate intuitive interfaces through which the user can select among the examples and interactively adjust those parts of the scene that do not match his requirements. We show that using a combination of our techniques, unusual and interesting results can be generated for moderately sized scenes with under an hour of user time. Scalability is demonstrated by sampling much larger scenes using modest offline computations.

Controllable and Scalable Simulation for Animation by Stephen John Chenney Doctor of Philosophy in Computer Science University of California at Berkeley Professor David A. Forsyth, Chair Simulation is an important means of generating animations. For example, we might use simulation to animate a virtual city in order to train emergency response personnel. Such an application requires the responsiveness and realism that simulation offers, and it also requires the ability to stage specific events in a very large animated environment. Traditional simulation technology fails on the latter count: it is difficult to direct a given simulation toward a desired outcome, and existing simulations rarely scale well to large virtual worlds. This thesis addresses controllable and scalable simulation for the purposes of computer animation. We describe a technique for directing the outcome of simulations by formulating the problem as one of probabilistic sampling. A Markov chain Monte Carlo (MCMC) algorithm is used to perform the sampling, which allows the generation of multiple animations from a desired distribution. Furthermore, if the distribution assigns probabilities according to the plausibility of an animation, then we can be certain that most of the sampled animations will appear reasonable to a viewer. A range of examples are presented from the domain of collision intensive rigid-body simulation, the majority of which could not be produced using previous technology. We also describe a new rigid-body simulation algorithm that was developed for this work. Scalable simulation is achieved through simulation culling, a method for focusing the computational effort on visible parts of a simulation. Aspects of the simulation that are not in view are not explicitly computed, thu...

Accuracy is the ubiquitous goal of dynamic simulation, in order to yield the "correct" motion. But for creating animation, what is really of interest is "plausible" motion, which is somewhat different. We discuss what we mean by plausible simulation, how it differs from "accurate" simulation, and whywe think it's a worthwhile area to study. The discussion touches on questions of physically plausible vs. visually plausible motion, plausible simulation in a noisy or textured environment, and probability measures for motion, as well as issues for forward and inverse problems. 1 Introduction Simulation is generally used in the context of a predictive model of behavior: given a precise description of a real-world situation, try to determine computationally what would really happen. When designing airplane parts, for example, accuracy of the model and of the simulation are critically important. However computer graphics animation has different needs, requiring a slightly different ou...

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