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The introduction of gestures as a supplementary input modality has become of increasing interest to human computer interaction design, especially for 3D computer environments. This thesis describes the concepts and development of a gesture recognition system based on the machine learning technique of Hidden Markov Models. Well-known from the field of speech recognition, this statistical method is employed in this thesis to represent and recognize predefined gestures. Within this work, gestures are defined as symbols, such as simple geometric shapes or Roman letters. They are extracted from a stream of three-dimensional optical tracking data which is resampled, reduced to 2D and quantized to be used as input to discrete Hidden Markov Models. A set of prerecorded training data is used to learn the parameters of the models and recognition is achieved by evaluating the trained models. The devised system was used to augment an existing virtual reality prototype application which serves as a demonstration and development platform for the VRGeo consortium. The consortium's goal is to investigate and utilize the benefits of virtual reality technology for the oil and gas industry. An isolated test of the system with seven gestures showed accuracies of up to 98.57% and the review from experts in the fields of virtual reality and geophysics was predominantly positive.
Along with the success of the digitally revived stereoscopic cinema, other events beyond 3D movies become attractive for movie theater operators, i.e. interactive 3D games. In this paper, we present a case that explores possible challenges and solutions for interactive 3D games to be played by a movie theater audience. We analyze the setting and showcase current issues related to lighting and interaction. Our second focus is to provide gameplay mechanics that make special use of stereoscopy, especially depth-based game design. Based on these results, we present YouDash3D, a game prototype that explores public stereoscopic gameplay in a reduced kiosk setup. It features live 3D HD video stream of a professional stereo camera rig rendered in a real-time game scene. We use the effect to place the stereoscopic effigies of players into the digital game. The game showcases how stereoscopic vision can provide for a novel depth-based game mechanic. Projected trigger zones and distributed clusters of the audience video allow for easy adaptation to larger audiences and 3D movie theater gaming.
Für die prototypische Erstellung von Virtual Reality (VR) Szenen auf Grundlage realer Umgebungen bieten sich Daten aus aktuellen Panorama-Kameras an. Diese Daten eignen sich jedoch nicht unmittelbar für die Integration in eine Game Engine. Wir stellen daher ein projektionsbasiertes Verfahren vor, mit dem Bilder und Videos im Fischaugenformat, wie sie z.B. die 360 Kamera Ricoh Theta erstellt, ohne Konvertierung in Echtzeit mit Hilfe der Unity Game Engine visualisiert werden können. Es wird weiterhin gezeigt, dass ein Panoramabild mit diesem Verfahren leicht manuell um grobe Tiefeninformation erweitert werden kann, sodass bei einer Darstellung in VR ein grober räumlicher Eindruck der Szene für einfach prototypische Umsetzungen ermöglicht wird.
Simulating eye movements for virtual humans or avatars can improve social experiences in virtual reality (VR) games, especially when wearing head mounted displays. While other researchers have already demonstrated the importance of simulating meaningful eye movements, we compare three gaze models with different levels of fidelity regarding realism: (1) a base model with static fixation and saccadic movements, (2) a proposed simulation model that extends the saccadic model with gaze shifts based on a neural network, and (3) a user's real eye movements recorded by a proprietary eye tracker. Our between-groups design study with 42 subjects evaluates impact of eye movements on social VR user experience regarding perceived quality of communication and presence. The tasks include free conversation and two guessing games in a co-located setting. Results indicate that a high quality of communication in co-located VR can be achieved without using extended gaze behavior models besides saccadic simulation. Users might have to gain more experience with VR technology before being able to notice subtle details in gaze animation. In the future, remote VR collaboration involving different tasks requires further investigation.
Traffic simulations for virtual environments are concerned with the behavior of individual traffic participants. The complexity of behavior in these simulations is often rather simple to abide by the constraints of processing resources. In sophisticated traffic simulations, the behavior of individual traffic participants is also modeled, but the focus lies on the overall behavior of the entire system, e.g. to identify possible bottle necks of traffic flow [8].
Integration of Multi-modal Cues in Synthetic Attention Processes to Drive Virtual Agent Behavior
(2017)
Realism and plausibility of computer controlled entities in entertainment software have been enhanced by adding both static personalities and dynamic emotions. Here a generic model is introduced which allows the transfer of findings from real-life personality studies to a computational model. This information is used for decision making. The introduction of dynamic event-based emotions enables adaptive behavior patterns. The advantages of this new model have been validated with a four-way crossroad in a traffic simulation. Driving agents using the introduced model enhanced by dynamics were compared to agents based on static personality profiles and simple rule-based behavior. It has been shown that adding an adaptive dynamic factor to agents improves perceivable plausibility and realism. It also supports coping with extreme situations in a fair and understandable way.
Gone But Not Forgotten: Evaluating Performance and Scalability of Real-Time Mesoscopic Agents
(2020)
Using virtual environment systems for road safety education requires a realistic simulation of road traffic. Current traffic simulations are either too restricted in their complexity of agent behavior or focus on aspects not important in virtual environments. More importantly, none of them are concerned with modeling misbehavior of traffic participants which is part of every-day traffic and should therefore not be neglected in this context. We present a concept for a traffic simulation that addresses the need for more realistic agent behavior with regard to road safety education. The two major components of this concept are a simulation of persistent agents which minimizes computational overhead and a model of cognitive processes of human drivers combined with psychological personality profiles to allow for individual behavior and misbehavior.
Realism and plausibility of computer controlled entities in entertainment software have been enhanced by adding both static personalities and dynamic emotions. Here a generic model is introduced that allows findings from real-life personality studies to be transferred to a computational model. Adaptive behavior patterns are enabled by introducing dynamic event-based emotions. The advantages of this model have been validated using a four-way crossroad in a traffic simulation. Driving agents using the introduced model enhanced by dynamics were compared to agents based on static personality profiles and simple rule-based behavior. The results show that adding a dynamic factor to agents improves perceivable plausibility and realism.
Perception is one of the most important cognitive capabilities of an entity since it determines how an entity perceives its environment. The presented work focuses on providing cost efficient but realistic perceptual processes for intelligent virtual agents (IVAs) or NPCs with the goal of providing a sound information basis for the entities' decision making processes. In addition, an agent-central perception process should rovide a common interface for developers to retrieve data from the IVAs' environment. The overall process is evaluated by applying it to a scenario demonstrating its benefits. The evaluation indicates, that such a realistically simulated perception process provides a powerful instrument to enhance the (perceived) realism of an IVA's simulated behavior.
Traffic simulations are typically concerned with modeling human behavior as closely as possible to create realistic results. In conventional traffic simulations used for road planning or traffic jam prediction only the overall behavior of an entire system is of interest. In virtual environments, like digital games, simulated traffic participants are merely a backdrop to the player’s experience and only need to be “sufficiently realistic”. Additionally, restricted computational resources, typical for virtual environment applications, usually limit the complexity of simulated behavior in this field. More importantly, two integral aspects of real-world traffic are not considered in current traffic simulations from both fields: misbehavior and risk taking of traffic participants. However, for certain applications like the FIVIS bicycle simulator, these aspects are essential.
Traditionally traffic simulations are used to predict traffic jams, plan new roads or highways, and estimate road safety. They are also used in computer games and virtual environments. There are two general concepts of modeling traffic: macroscopic and microscopic modeling. Macroscopic traffic models take vehicle collectives into account and do not consider individual vehicles. Parameters like average velocity and density are used to model the flow of traffic. In contrast, microscopic traffic models consider each vehicle individually. Therefore, vehicle specific parameters are of importance, e.g. current velocity, desired velocity, velocity difference to the lead vehicle, individual time gap.
Populating virtual worlds with intelligent agents can drastically improve a user's sense of presence. Applying these worlds to virtual training, simulations, or (serious) games, often requires multiple agents to be simulated in real time. The process of generating believable agent behavior starts with providing a plausible perception and attention process that is both efficient and controllable. We describe a conceptual framework for synthetic perception that specifically considers the mentioned requirements: plausibility, real-time performance, and controllability. A sample implementation will focus on sensing, attention, and memory to demonstrate the framework's capabilities in a real-time game engine scenario. A combination of dynamic geometric sensing and false coloring with static saliency information is provided to exemplify the collection of environmental stimuli. The subsequent attention process handles both bottom-up processing and task-oriented, top-down factors. Behavioral results can be influenced by controlling memory and attention The example case is demonstrated and discussed alongside future extensions.