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We present Energy Redistribution (ER) sampling as an unbiased method to solve correlated integral problems. ER sampling is a hybrid algorithm that uses Metropolis sampling-like mutation strategies in a standard Monte Carlo integration setting, rather than resorting to an intermediate probability distribution step. In the context of global illumination, we present Energy Redistribution Path Tracing (ERPT). Beginning with an inital set of light samples taken from a path tracer, ERPT uses path mutations to redistribute the energy of the samples over the image plane to reduce variance. The result is a global illumination algorithm that is conceptually simpler than Metropolis Light Transport (MLT) while retaining its most powerful feature, path mutation. We compare images generated with the new technique to standard path tracing and MLT.

We present Energy Redistribution (ER) sampling as an unbiased method to solve correlated integral problems. ER sampling is a hybrid algorithm that uses Metropolis sampling-like mutation strategies in a standard Monte Carlo integration setting, rather than resorting to an intermediate probability distribution step. In the context of global illumination, we present Energy Redistribution Path Tracing (ERPT). Beginning with an inital set of light samples taken from a path tracer, ERPT uses path mutations to redistribute the energy of the samples over the image plane to reduce variance. The result is a global illumination algorithm that is conceptually simpler than Metropolis Light Transport (MLT) while retaining its most powerful feature, path mutation. We compare images generated with the new technique to standard path tracing and MLT.

The ability for a given agent to adapt on‐line to better interact with another agent is a difficult and important problem. This problem becomes even more difficult when the agent to interact with is a human, because humans learn quickly and behave nondeterministically. In this paper, we present a novel method whereby an agent can incrementally learn to predict the actions of another agent (even a human), and thereby can learn to better interact with that agent. We take a case‐based approach, where the behavior of the other agent is learned in the form of state–action pairs. We generalize these cases either through continuous k‐nearest neighbor, or a modified bounded minimax search. Through our case studies, our technique is empirically shown to require little storage, learn very quickly, and be fast and robust in practice. It can accurately predict actions several steps into the future. Our case studies include interactive virtual environments involving mixtures of synthetic agents and humans, with cooperative and/or competitive relationships.

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We present a particle-based algorithm for modeling highly viscous liquids. Using a numerical time-integration of particle acceleration and velocity, we apply external forces to particles and use a convenient organization, the adhesion matrix, to represent forces between different types of liquids and objects. Viscosity is handled by performing a momentum exchange between particle pairs such that momentum is conserved. Volume is maintained by iteratively adjusting particle positions after each time step. We use a two-tiered approach to time stepping that allows particle positions to be updated many times per frame while expensive operations, such as calculating viscosity and adhesion, are done only a few times per frame. The liquid is rendered using an implicit surface polygonization algorithm, and we present an implicit function that convolves the liquid surface with a Gaussian function, yielding a smooth liquid skin.

Adaptation (online learning) by autonomous virtual characters, due to interaction with a human user in a virtual environment, is a difficult and important problem in computer animation. In this article we present a novel multi-level technique for fast character adaptation. We specifically target environments where there is a cooperative or competitive relationship between the character and the human that interacts with that character.In our technique, a distinct learning method is applied to each layer of the character's behavioral or cognitive model. This allows us to efficiently leverage the character's observations and experiences in each layer. This also provides a convenient temporal distinction between what observations and experiences provide pertinent lessons for each layer. Thus the character can quickly and robustly learn how to better interact with any given unique human user, relying only on observations and natural performance feedback from the environment (no explicit feedback from the human). Our technique is designed to be general, and can be easily integrated into most existing behavioral animation systems. It is also fast and memory efficient.