For many systems that produce physically based animations, plausibility rather than accuracy is acceptable. We consider the problem of evaluating the visual quality of animations in which physical parameters have been distorted or degraded, either unavoidably due to real-time frame-rate requirements, or intentionally for aesthetic reasons. To date, no generic means of evaluating or predicting the fidelity, either physical or visual, of the dynamic events occurring in an animation exists. As a first step towards providing such a metric, we present a set of psychophysical experiments that established some thresholds for human sensitivity to dynamic anomalies, including angular, momentum and spatio-temporal distortions applied to simple animations depicting the elastic collision of two rigid objects. In addition to finding significant acceptance thresholds for these distortions under varying conditions, we identified some interesting biases that indicate non-symmetric responses to these distortions (e.g., expansion of the angle between postcollision trajectories was preferred to contraction and increases in velocity were preferred to decreases). Based on these results, we derived a set of probability functions that can be used to evaluate the visual fidelity of a physically based simulation. To illustrate how our results could be used, two simple case studies of simulation levels of detail and constrained dynamics are presented.

We present a method for synthesizing animations of autonomous space, water, and land-based vehicles in games or other interactive simulations. Controlling the motion of such vehicles to achieve a desirable behavior is difficult due to the constraints imposed by the system dynamics. We combine real-time path planning and a simplified physics model to automatically compute control actions to drive a vehicle from an input state to desirable output states based on a behavior cost function. Both offline trajectory preprocessing and online search are used to build an animation framework suitable for interactive vehicle simulations. We demonstrate synthesized animations of simulated spacecraft and automobiles performing a variety of autonomous behaviors, including Seek, Pursue, Avoid, Avoid Obstacle, and Flee. We also explore several enhancements to the basic planning algorithm and examine the resulting tradeoffs in runtime performance and quality of the generated motion.

The simulation of large crowds of humans is important in many fields of computer graphics, including real-time applications such as games, as they can breathe life into otherwise static scenes and enhance believability. We present a novel hybrid rendering system for crowds that solves the classic problem of degraded quality of image-based representations at close distances by building an impostor rendering system on top of a full, geometry-based, human animation system. This enables almost imperceptible switching between the two representations based on a “pixel to texel” ratio, with minimal popping artefacts. Seamless interchanges are further facilitated by exploiting programmable graphics hardware to efficiently enhance the realism and variety of the dynamically-lit impostors, thereby also improving on existing impostor techniques. To test our system, our virtual crowds are embedded in an urban simulation system (as shown in Figure 1). The results demonstrate a system

Holonic Multi-Agent Systems (HMAS) are a convenient and relevant way to analyze, model and simulate complex and open systems. Accurately simulate in real-time complex systems, where a great number of entities interact, requires extensive computational resources and often distribution of the simulation over various computers. A possible solution to these issues is multilevel simulation. This kind of simulation aims at dynamically adapting the level of entities ’ behaviors (microscopic, macroscopic) while being as faithful as possible to the simulated model. We propose a holonic organizational multilevel model for real-time simulation of complex systems by exploiting the hierarchical and distributed properties of the holarchies. To fully exploit this model, we estimate the deviation of simulation accuracy between two adjacent levels through physics-based indicators. These indicators will then allow us to dynamically determine the most suitable level for each entity in the application to maintain the best compromise between simulation accuracy and available resources. Finally a 3D real-time multilevel simulation of pedestrians is presented as well as a discussion of experimental results. 1

Graph-based approaches for sequencing motion capture data have produced some of the most realistic and controllable character motion to date. Most previous graph-based approaches have employed a run-time global search to find paths through the motion graph that meet user-defined constraints such as a desired locomotion path. Such searches do not scale well to large numbers of characters. In this paper, we describe a locomotion approach that benefits from the realism of graph-based approaches while maintaining basic user control and scaling well to large numbers of characters. Our approach is based on precomputing multiple least cost sequences from every state in a state-action graph. We store these precomputed sequences in a data structure called a mobility map and perform a local search of this map at run-time to generate motion sequences in real time that achieve user constraints in a natural manner. We demonstrate the quality of the motion through various example locomotion tasks including target tracking and collision avoidance. We demonstrate scalability by animating crowds of up to 150 rendered articulated walking characters at real-time rates.

There are significant differences between the art of animating for linear media such as film and video and the art of animating for interactive media such as computer and video games. In particular, these differences arise from the shift from linear characters to autonomous interactive characters. This article describes differences between linear animation and interactive animation in several areas of character design – character intelligence, emotional expressiveness, navigation, transitions among animations, and multi-character interaction. These differences provide insight into the processes of both forms of animation and the final products that they create, and may provide a starting point for linear animators interested in becoming familiar with interactive animation.

ABSTRACT: Simulation developers are forced to make assumptions about how their simulations will be used and possibly revised to support reuse. Even when developers are aware of potential future adaptations for reuse, current programming languages do not support expression of design alternatives reflecting those adaptations. One can use program documentation to describe them, but documentation does not support automatic simulation transformation. Previously we have described COERCE, a semi-automated simulation transformation technology that supports the capture of design alternatives and the subsequent search and exploitation of these alternatives in order to accomplish desired changes in simulation behavior. In this paper, we propose capturing these design alternatives in programming language extensions called flexible points. With metadata about flexible points embedded in simulation code, COERCE-based software tools can preprocess the code, present information about flexible points to the user, and support semi-automatic evaluation of the fitness of different design alternatives for the new requirements. The programming language extensions we describe in this paper would advance our goal of automating simulation coercion to the extent possible. Semi-automated coercion of simulations, in turn, would greatly enhance user experience with simulation reuse. 1.

Simulation is an appropriate approach for studying complex systems that are inacessible through direct observations and measurements. In a simulation involving a great number of interacting entities, it is difficult to create a reliable and tractable abstraction of the real reference system. One of the involved problems is amount of computational resources required to handle microscopic simulation of large number of entities. One solution is to use macroscopic models. However, this type of models may be at hand unavailable or not reliable, or it doesn’t allow observations of individual behaviours. In this paper a multilevel simulation model is proposed to dynamically adapt the level of simulated behaviours while being as faithful as possible to the reference model. Our approach is based on Holonic Multi-Agent Systems and provides a generic scheduling model for multilevel simulations. 1

vehicles (left,middle). We can simulate the motion of 2, 000 human agents at interactive rates. Crowds react as dynamics obstacles approach their path (right). We present a real-time algorithm for simulating heterogeneous crowds with potentially distinct individual behavior characteristics and goals. Our approach combines global motion planning with a generalized pedestrian dynamics model to synthesize motion for numerous groups of people in complex dynamic environments. Inspired by self-organization phenomena in crowds, we introduce “Pedestrian Levels of Detail ” to accelerate large-scale simulation of individual agents, while preserving natural collective behaviors observed in real crowds. We highlight the performance of our algorithm on heterogeneous crowds in urban environments.