Perceptually Driven Simulation

This dissertation describes, implements and analyzes a comprehensive system for perceptually-driven virtual reality simulation, based on algorithms which dynamically adjust level of detail (LOD) for entity simulation in order to maximize simulation realism as perceived by the viewer. First we review related work in simulation LOD, and describe the weaknesses of the analogy that has traditionally been drawn between simulation LOD and graphical LOD. We describe the process of perceptual criticality modeling for quantitatively estimating the relative importance of different entities in maintaining perceived realism and predicting the consequences of LOD transitions on perceived realism. We present heuristic cognitive models of human perception, memory, and attention to perform this modeling. We then propose the LOD Trader , a framework for perceptually driven LOD selection and an online approximation algorithm for efficiently identifying useful LOD transitions. We then describe alibi generation , a method of retroactively elaborating a human agent\u27s behavior to maintain its realism under prolonged scrutiny from the viewer, and discuss its integration into a heterogeneous perceptually driven simulation. We then present the Marketplace simulation system and describe how perceptually driven simulation techniques were used to maximize perceived realism, and evaluate their success in doing so. Finally, we summarize the dissertation work performed and its expected contributions to real-time modeling and simulation environments

Perceptually Realistic Behavior through Alibi Generation

Real-time pedestrian simulation for open-world games involves aggressive behavior simplification and culling to keep computational cost under control, but it is diffficult to predict whether these techniques will become unrealistic in certain situations. We propose a method of perceptually simulating highly realistic pedestrian behavior in virtual cities in real- time. Designers build a highly realistic simulation, from which a perceptually identical “perceptual simulation” is generated. Although the perceptual simulation simulates only a small portion of the world at a time, and does so with inexpensive approximations, it can be statistically guaranteed that the results are perceptually indistinguishable from those of the original simulation

Generating Plausible Individual Agent Movements From Spatio-Temporal Occupancy Data

We introduce the Spatio-Temporal Agent Motion Model, a datadriven representation of the behavior and motion of individuals within a space over the course of a day. We explore different representations for this model, incorporating different modes of individual behavior, and describe how crowd simulations can use this model as source material for dynamic and realistic behaviors

CRAM It! A Comparison of Virtual, Live-Action and Written Training Systems for Preparing Personnel to Work in Hazardous Environments

In this paper we investigate the utility of an interactive, desktopbased virtual reality (VR) system for training personnel in hazardous working environments. Employing a novel software model, CRAM (Course Resource with Active Materials), we asked participants to learn a specific aircraft maintenance task. The evaluation sought to identify the type of familiarization training that would be most useful prior to hands on training, as well as after, as skill maintenance. We found that participants develop an increased awareness of hazards when training with stimulating technology – in particular (1) interactive, virtual simulations and (2) videos of an instructor demonstrating a task – versus simply studying (3) a set of written instructions. The results also indicate participants desire to train with these technologies over the standard written instructions. Finally, demographic data collected during the evaluation elucidates future directions for VR systems to develop a more robust and stimulating hazard training environment

Real-Time Evacuation Simulation in Mine Interior Model of Smoke and Action

Virtual human crowd models have been used in the simulation of building and urban evacuation, but have not yet applied to underground coal mine operations and escape situations with emphasis on smoke, fires and physiological behaviors. We explore this through a real-time simulation model, MIMOSA (Mine Interior Model Of Smoke and Action), which integrates an underground coal mine virtual environment, a fire and smoke propagation model, and a human physiology and behavior model. Each individual agent has a set of physiological parameters as variables of time and environment, simulating a miner’s physiological condition during normal operations as well as during emergencies due to fire and smoke. To obtain appropriate agent navigation in the mine environment, we have extended the HiDAC framework (High- Density Autonomous Crowds) navigation from a grid-based cell-portal graph to a geometrybased portal path and integrated a novel cellportal and shortest path visibility algorithm

Abstract Photorealistic Lighting with Offset Radiance Transfer Mapping

We propose a precomputation-based approach for the real-time rendering of scenes that include a number of complex illumination phenomena, such as radiosity and subsurface scattering, and allows interactive modification of camera and lighting parameters. At the heart of our approach lies a novel parameterization of the rendering equation that is inherently supported by the modern GPU. During the pre-computation phase, we build a set of offset transfer maps based on the proposed parameterization, which approximate the complete radiance transfer function for the scene. The rendering phase is then reduced to a set of texture-blending and mapping operations that execute in real-time on the GPU. In contrast to the current state-of-the-art, which employs environment maps to produce global illumination, our approach uses arbitrary first-order lighting to compute a final lighting solution, and fully supports point and spot lights. To discretize the transfer maps, we develop an efficient method for generating and sampling C 0-continuous probability density functions from unordered data points. We believe that the contributions of this paper offer a significantly different approach to precomputed radiance transfer from those previously proposed

Generating plausible individual agent movements from spatio-temporal occupancy data

We introduce the Spatio-Temporal Agent Motion Model, a datadriven representation of the behavior and motion of individuals within a space over the course of a day. We explore different representations for this model, incorporating different modes of individual behavior, and describe how crowd simulations can use this model as source material for dynamic and realistic behaviors