As large-scale online simulations such as Second Life and World of Warcraft are gaining economic significance, there is a growing incentive for attacks against such simulation software. We focus on attacks against the semantic integrity of the simulation. This class of attacks exploits the client-server architecture and is specific to online simulations which, for performance reasons, have to delegate the detailed rendering of the simulated world to the clients. Attacks against semantic integrity often compromise the physical laws of the simulated world—enabling the user's simulation persona to fly, walk through walls, or to run faster than anybody else. We introduce the Secure Semantic Integrity Protocol (SSIP), which enables the simulation provider to audit the client computations. Then we analyze the security and scalability of SSIP. First, we show that under standard cryptographic assumptions SSIP will detect semantic integrity attacks. Second, we analyze the network overhead, and determine the optimum tradeoff between cost of bandwidth and audit frequency for our protocol.

In the computer gaming industry, large-scale simulations of realistic physical environments over the Internet have attained increasing importance. Networked virtual environments (NVEs) are typically based on a client-server architecture where part of the simulation workload is delegated to the clients. This architecture renders the simulation vulnerable to attacks against the semantic integrity of the simulation: malicious clients may attempt to compromise the physical and logical rules governing the simulation, or to alter the causality of events. This paper initiates the systematic study of semantic integrity in NVEs from a security point of view. We present a new provably secure semantic integrity protocol which enables the server system to audit the local computations of the clients on demand.

Abstract We present the Optimizing Control Variate (OCV) estimator, a new estimator for Monte Carlo rendering. Based upon a deterministic sampling framework, OCV allows multiple importance sampling functions to be combined in one algorithm. Its optimizing nature addresses a major problem with control variate estimators for rendering: users supply a generic correlated function which is optimized for each estimate, rather than a single highly tuned one that must work well everywhere. We demonstrate OCV with both direct lighting and irradiance‐caching examples, showing improvements in image error of over 35% in some cases, for little extra computation time. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism Color, shading, shadowing, and texture G.3 [Probability and Statistics]: Probabilistic Algorithms Keywords: direct lighting, deterministic mixture sampling, control variates

Abstract Crowd simulation for virtual environments offers many challenges centered on the trade‐offs between rich behavior, control and computational cost. In this paper we present a new approach to controlling the behavior of agents in a crowd. Our method is scalable in the sense that increasingly complex crowd behaviors can be created without a corresponding increase in the complexity of the agents. Our approach is also more authorable; users can dynamically specify which crowd behaviors happen in various parts of an environment. Finally, the character motion produced by our system is visually convincing. We achieve our aims with a situation‐based control structure. Basic agents have very limited behaviors. As they enter new situations, additional, situation‐specific behaviors are composed on the fly to enable agents to respond appropriately. The composition is done using a probabilistic mechanism. We demonstrate our system with three environments including a city street and a theater. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Animation

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

Scalable rendering of virtual environments requires culling objects that have no etTect on the view.This paper explores culling moving objects by not solving the equations of motion of objects that don't tiect the view.While this approach could be scalable for many kinds of environments, it raises two problems: consistency -ensuring that objects that come back into view do so in the right state -and completeness -ensuring that objects that would have entered the view volume as a result of their motions, do so.Solutions to these problems lie in studying the statistics of the motion of objects.We show strategies for addressing the problem of consistency, with a number of examples that illustrate both the difficulties involved in, and the potential gains to be obtained by, formulating a comprehensive approach.