Freeform User Interfaces for Graphical Computing

報告番号: 甲15222 ; 学位授与年月日: 2000-03-29 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: 博工第4717号 ; 研究科・専攻: 工学系研究科情報工学専

An engine selection methodology for high fidelity serious games

Serious games represent the state-of-the-art in the convergence of electronic gaming technologies with instructional design principles and pedagogies. Whilst the selection criteria for entertainment game engines are often transparent, due to the range of available platforms and engines an emerging challenge is the choice of platform for serious games, whose selection often has substantially different objectives and technical requirements depending upon context and usage. Additionally, the convergence of training simulations with serious gaming, made possible by increasing hardware rendering capacity, is enabling the creation of high-fidelity serious games which challenge existing design and instructional approaches. This paper highlights some of the differences between the technical requisites of high-fidelity serious and leisure games, and proposes a selection methodology based upon these emergent characteristics. The case study of part of a high-fidelity model of Ancient Rome is used to compare aspects of the four different game engines according to elements defined in the proposed methodology

Perceptually Modulated Level of Detail for Virtual Environments

Institute for Computing Systems ArchitectureThis thesis presents a generic and principled solution for optimising the visual complexity of any arbitrary computer-generated virtual environment (VE). This is performed with the ultimate goal of reducing the inherent latencies of current virtual reality (VR) technology. Effectively, we wish to remove extraneous detail from an environment which the user cannot perceive, and thus modulate the graphical complexity of a VE with little or no perceptual artifacts. The work proceeds by investigating contemporary models and theories of visual perception and then applying these to the field of real-time computer graphics. Subsequently, a technique is devised to assess the perceptual content of a computer-generated image in terms of spatial frequency (c/deg), and a model of contrast sensitivity is formulated to describe a user's ability to perceive detail under various conditions in terms of this metric. This allows us to base the level of detail (LOD) of each object in a VE on a measure of the degree of spatial detail which the user can perceive at any instant (taking into consideration the size of an object, its angular velocity, and the degree to which it exists in the peripheral field). Additionally, a generic polygon simplification framework is presented to complement the use of perceptually modulated LOD. The efficient implementation of this perceptual model is discussed and a prototype system is evaluated through a suite of experiments. These include a number of low-level psychophysical studies (to evaluate the accuracy of the model), a task performance study (to evaluate the effects of the model on the user), and an analysis of system performance gain (to evaluate the effects of the model on the system). The results show that for the test application chosen, the frame rate of the simulation was manifestly improved (by four to five-fold) with no perceivable drop in image fidelity. As a result, users were able to perform the given wayfinding task more proficiently and rapidly. Finally, conclusions are drawn on the application and utility of perceptually-based optimisations; both in reference to this work, and in the wider context

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Device-based decision-making for adaptation of three-dimensional content

The goal of this research was the creation of an adaptation mechanism for the delivery of three-dimensional content. The adaptation of content, for various network and terminal capabilities - as well as for different user preferences, is a key feature that needs to be investigated. Current state-of-the art research of the adaptation shows promising results for specific tasks and limited types of content, but is still not well-suited for massive heterogeneous environments. In this research, we present a method for transmitting adapted three-dimensional content to multiple target devices. This paper presents some theoretical and practical methods for adapting three-dimensional content, which includes shapes and animation. We also discuss practical details of the integration of our methods into MPEG-21 and MPEG-4 architecture

Data-driven modelling of perceptual properties of 3D shapes

The recent surge in 3D content generation has led to the evolution of difficult to search, organise and re-use massive online 3D visual content libraries. We explore crowdsourcing and machine learning techniques to help alleviate these difficulties by focusing on the visual perceptual properties of 3D shapes. We study “style similarity” and “aesthetics” as two fundamental perceptual properties of 3D shapes and build data-driven models. We rely on crowdsourcing platforms to collect large number of human judgements on style matching and aesthetics of 3D shapes. The judgement data collected directly from humans is used to learn metrics of style matching and aesthetics. Our style similarity measure can be used to compute style distance between a pair of input 3D shapes. In contrast to previous work, we incorporate colour and texture in addition to geometric features to build a colour and texture aware style similarity metric. We also experiment with learning objective and personalised style metrics 3D shapes. The application prototypes we build demonstrate the use of style based search and scene composition. Further, our style distance metric is built iteratively to consume lesser amount of human style judgement data compared to previous methods. We study the problem of building a data-driven model of 3D shape aesthetics in two steps. We first focus on designing a study to crowdsource human aesthetics judgement data. We then formulate a deep learning based strategy to learn a measure of 3D shape aesthetics from collected data. The results of the study in first step helped us choose an appropriate shape representation i.e. voxels as an input to deep neural networks for learning a measure of visual aesthetics. In the same crowdsourcing study, we experiment with the use of polygonal, volumetric, and point based shape representations to create shape stimuli to collect and compare human shape aesthetics judgements. On analysis of the collected data we found that that humans can reliably distinguish more aesthetic shape in a pair even from coarser shape representations such as voxels. This observation implies that detailed shape representations are not needed to compare aesthetics in pairs. The aesthetic value of a 3D shape has traditionally been explored in terms of specific visual features (or handcrafted features) such as curvature and symmetry. For example, more symmetric and curved shapes are considered aesthetic compared to less curved and symmetric shapes. We call such properties as pre-existing notion (or rules) of aesthetics. In order to develop a measure of perceptual aesthetics of 3D shapes which is independent of any pre-existing notion or shape features, we train deep neural networks directly on human aesthetics judgement data. We demonstrate the usefulness of the learned measure by designing applications to rank a collection of shapes based on their aesthetics scores and interactively build scenes using shapes with high aesthetics scores. The overarching goal of this thesis is to demonstrate the use of machine learning and crowdsourcing approaches to build data-driven models of visual perceptual properties of 3D shapes for applications in search, organisation, scene composition, and visualisation of 3D shape data present in ever increasing online 3D shape content libraries. We believe that our exploration of perceptual properties of 3D shapes will motivate further research by looking into other important perceptual properties related to our vision system and will also fuel development of techniques to automatically enhance such properties of a given 3D shape