A immersive VR-art gallery that investigates and proposes new ways of displaying digital art.

No description available.

Though the cinematic genre of horror was not designated until the twentieth century, elements of this genre have appeared onstage since the time of the Greeks. Theatre history is rife with examples of theatrical ghosts and horrors, whose ever-changing representation indicates society's evolving relationship to and expectation for horror onstage. In 2019, Virginia Tech presented the installation Poe's Shadows, which combined elements of traditional theatre, original art, and innovative technology to present an immersive experience of Edgar Allan Poe's work. This production was a unique collaborative work that combined the creative labor of both faculty and students, while also invoking past horror theatre techniques and technologies. The properties of the Cube performance space allowed the Poe's Shadows creative team to imitate hand-cranked panoramas, magic lantern shows, and shadow plays, while also using sound effects and narration that combined elements of theatrical tradition and ghost shows. By studying the history of Poe's Shadows, as well as the reception of the installation, one can see how the theatre's evolving relationship with horror is effected by audience demand and expectation, as well as newly available technologies. / Master of Arts / Though the horror genre is most often associated with books and films, elements of the genre have been present onstage for thousands of years. Furthermore, studying these theatrical ghosts and ghouls—and how they were represented onstage— can help contemporary audiences understand historical anxieties and expectations. In 2019, Virginia Tech presented the installation Poe’s Shadows, which combined elements of traditional theatre, original art, and innovative technology to present an immersive experience of Edgar Allan Poe’s work. This production was a unique collaborative work that combined the creative labor of both faculty and students, while also invoking past horror theatre techniques such as hand-cranked panoramas, magic lantern shows, and shadow plays, accompanied by with sound effects and narration that combined elements of theatrical tradition and ghost shows. By studying the history of Poe’s Shadows, as well as the reception of the installation, one can see how the theatre’s evolving relationship with horror is effected by audience expectation and newly available technologies.

Calvary is a 3D fictional cathedral that is based around Christian beliefs. It is a new way to experience spiritual landmarks, fictional or nonfictional, using virtual reality. The goal is to allow the viewer to experience this space wherever they are located and to create a dialogue about who God is to them. / Master of Fine Arts

"3D Digitized Romanesque Ermita in Virtual Reality" is a digital exploration of the historical, architectural, and cultural significance of a medieval Romanesque church in Valladolid, Spain. In this project, I recreate Iglesia de Nuestra Señora de la Anunciada, a reconstructed Catholic monastery. A digital replica of the church, produced by photogrammetry in the summer of 2014, has been created in a virtual environment and is experienced through immersive technologies. The use of a head-mounted display is a wearable device in which users view environments three dimensionally, while a Qualisys motion tracking system allows users to physically walk through the virtual world. Additionally, this project explores the potential for the use of virtual reality as a learning tool in classroom and museum settings. / Master of Fine Arts

"Fabricated Preservation" aims to push the boundaries with traditional theses, creating a multi-layered experience that blends fact and fiction through a performance that centers around environmental storytelling in virtual reality. The experience questions the balance of theatrical elements in traditional storytelling which forefront text and human characters over architecture, props, and environment, and critically examines how an environment can play a crucial role in a narrative. The narrative itself focuses on one question: if a genealogy company had access to past environments via time travel, what new information could we learn about our ancestors?  The normal perceptions of a "game" are challenged, introducing real world elements to trick the audience and subtly influence how they navigate a virtual space. A complex fictional character is introduced through the performance and developed through the environment, with the hopes that the audience will gain some emotion toward them: either connecting with the character as if they were a close friend, or feeling unsettled that they observed the character's realistic personal space. This voyeuristic theme weaves its way through each layer of the storytelling, poking at the audience's morals with the hopes that they will question the experience around them.  Above all, the main goal of Fabricated Preservation is to challenge the audience to mentally engage with the virtual experience, by paying attention to the details of their environment and constructing their own version of a narrative from those details. / Master of Fine Arts / Fabricated Preservation examines how an environment can play a crucial role in a narrative. An environmental story was created that centers around a fictional character, influenced by a close friend's life. Virtual reality was used to allow the audience to immerse themselves more within a virtual bedroom environment, using virtual props to convey the personality of the character. A fictional genealogy company called The Fifth Turning was also created to convey that environmental story through a different perspective to the audience. There are two main stories that go hand in hand with this experience: the primary story is the life of a college girl in the 1990s. The secondary story is of the fictional company, the Fifth Turning, which uses time travel technology to access the bedroom environment of the college girl to obtain more personalized genealogy information.

Sensemaking of large textual information is a cognitively intensive task. Prior work has shown the value of large 2D high-resolution-display spaces in supporting the sensemaking process, by providing a "Space to Think" in which analysts can organize information and externalize their thought process. In this paper, we investigate how analysts use 3D immersive spaces for the same task. To this end, we conducted a user study where participants were asked to solve an intelligence analysis task using an Immersive Space to Think (IST) in a 3D virtual environment using an HMD. The study results show that the concepts of Space to Think extend naturally to 3D immersive space and that the 3D space offers some additional opportunities. With immersive space, analysts i) organized in a surrounding virtual sphere, ii) exploited the depth dimension for encoding relevance, and iii) traded-off between physical navigation and occlusion. / Master of Science / Sensemaking of large textual information is a cognitively intensive task. Prior work has shown the value of large 2D high-resolution-display spaces in supporting the sensemaking process, by providing a "Space to Think" in which analysts can organize information and externalize their thought process. In this paper, we investigate how analysts use 3D immersive spaces for the same task. To this end, we conducted a user study where participants were asked to solve an intelligence analysis task using an Immersive Space to Think (IST) in a 3D virtual environment using an HMD. The study results show that the concepts of Space to Think extend naturally to 3D immersive space and that the 3D space offers some additional opportunities. With immersive space, analysts i) organized in a surrounding virtual sphere, ii) exploited the depth dimension for encoding relevance, and iii) traded-off between physical navigation and occlusion.

Sensemaking is the way in which we understand the world around us. Pirolli and Card developed a sensemaking model related to intelligence analysis, which involves taking raw, unstructured data, analyzing it, and presenting a report of the findings. With lower-cost immersive technologies becoming more popular, new opportunities exist to leverage embodied and distributed cognition to better support sensemaking by providing vast, immersive space for creating meaningful schemas (organizational structures) during an analysis task. This work builds on prior work in immersive analytics on the concept of Immersive Space to Think (IST), which provides analysts with immersive space to physically navigate and use to organize information during a sensemaking task. In this work, we performed several studies that aimed to understand how IST supports sensemaking and how we can develop additional features to better aid analysts while they complete sensemaking in immersive analytics systems, focusing on non-quantitative data analysis. In a series of exploratory user studies, we aimed to understand how users' sensemaking process evolves during multiple session analyses, which identified how the participants refined their use of the immersive space into later stages of the sensemaking process. Another exploratory user study highlighted how professional analysts and novice users share many similarities in immersive analytic tool usage during sensemaking within IST. In addition to looking at multi-session analysis tasks, we also explored how sensemaking strategies change as users become more familiar with the immersive analytics tool usage in an exploratory study that utilized multiple analysis tasks completed over a series of three user study sessions. Lastly, we conducted a comparative user study to evaluate how the addition of new organizational features, clustering, and linking affect sensemaking within IST. Overall, our studies expanded the IST tool set and gathered an enhanced understanding of how immersive space is utilized during analysis tasks within IST. / Doctor of Philosophy / Sensemaking is a process we do in our daily lives. It is how we understand the world around us, make decisions, and complete complex analyses, like journalists writing stories or detectives solving cases. Sensemaking involves gathering information, making sense of it, developing hypotheses, and drawing conclusions, similar to writing a report. This work builds on prior work in Immersive Space to Think (IST), which is a concept of using immersive technologies (Virtual /Augmented Reality) to support sensemaking by providing vast 3D space for organizing the data used in a sensemaking task. Additionally, using these technologies to support sensemaking provides benefits such as increased space for analysis, increased engagement, and natural user interaction, which allow us to interact with information used during sensemaking tasks in new ways. In IST, users are able to move virtual documents around in the space around them to support their analysis process. In this work, we ran a study focused on multi-session analysis within IST, revealing how users refined their document placements over time while completing sensemaking tasks within IST. We also ran a study to understand how professional analysts' and novice users' analysis with IST differed in the IST tool usage. In another user study, we explored how users' strategies for sensemaking and document layouts changed as they became more familiar with the IST tool. Lastly, we conducted a comparative user study to evaluate how new features like clustering and linking affected analysis within IST. Overall, our work contributed to an enhanced understanding of how immersive space is utilized during analysis tasks within IST.

As one of the earliest attempts to apply immersive technology in environmental communication, this design research project tries to answer the following research question: how do we communicate the connections between food-related behaviors and environmental impacts through immersive storytelling? During the project, an immersive story called "Trik’s' Party" for dome shows and a journey map of an immersive visitor experience are created. These design outcomes and this paper are built on the knowledge of scientific findings, communication methods, content creations, and service design. To support the creation process, new sketching, storyboarding and prototyping methods were developed for dome content creations. The core message of this paper is that effective environmental communication is not just about informing the public about facts and data from scientific studies. It is also about giving individuals and communities the knowledge, tools and spaces to develop a vision of their own future. Immersive storytelling is one of the methods for creating these spaces. It has a large potential to raise public empathy with other people and their future-self when the long-term and abstract impacts of the environmental problems become more visible and comprehensible in an imaginary space.

Conduite dans le but de rendre les robots comme socio-communicatifs, les chercheurs ont cherché à mettre au point des robots dotés de compétences sociales et de «bon sens» pour les rendre acceptables. Cette intelligence sociale ou «sens commun» du robot est ce qui finit par déterminer son acceptabilité sociale à long terme.Cependant, ce n'est pas commun. Les robots peuvent donc seulement apprendre à être acceptables avec l'expérience. Cependant, en enseignant à un humanoïde, les subtilités d'une interaction sociale ne sont pas évidentes. Même un échange de dialogue standard intègre le panel le plus large possible de signes qui interviennent dans la communication et sont difficiles à codifier (synchronisation entre l'expression du corps, le visage, le ton de la voix, etc.). Dans un tel scénario, l'apprentissage du modèle comportemental du robot est une approche prometteuse. Cet apprentissage peut être réalisé avec l'aide de techniques d'IA. Cette étude tente de résoudre le problème de l'apprentissage des modèles comportementaux du robot dans le paradigme automatisé de planification et d'ordonnancement (APS) de l'IA. Dans le domaine de la planification automatisée et de l'ordonnancement (APS), les agents intelligents nécessitent un modèle d'action (plans d'actions dont les exécutions entrelacées effectuent des transitions de l'état système) afin de planifier et résoudre des problèmes réels. Au cours de cette thèse, nous présentons deux nouveaux systèmes d'apprentissage qui facilitent l'apprentissage des modèles d'action et élargissent la portée de ces nouveaux systèmes pour apprendre les modèles de comportement du robot. Ces techniques peuvent être classées dans les catégories non optimale et optimale. Les techniques non optimales sont plus classiques dans le domaine, ont été traitées depuis des années et sont de nature symbolique. Cependant, ils ont leur part de quirks, ce qui entraîne un taux d'apprentissage moins élevé que souhaité. Les techniques optimales sont basées sur les progrès récents dans l'apprentissage en profondeur, en particulier la famille à long terme (LSTM) de réseaux récurrents récurrents. Ces techniques sont de plus en plus séduisantes par la vertu et produisent également des taux d'apprentissage plus élevés. Cette étude met en vedette ces deux techniques susmentionnées qui sont testées sur des repères d'IA pour évaluer leurs prouesses. Ils sont ensuite appliqués aux traces HRI pour estimer la qualité du modèle de comportement du robot savant. Ceci est dans l'intérêt d'un objectif à long terme d'introduire l'autonomie comportementale dans les robots, afin qu'ils puissent communiquer de manière autonome avec les humains sans avoir besoin d'une intervention de «magicien». / Driven with the objective of rendering robots as socio-communicative, there has been a heightened interest towards researching techniques to endow robots with social skills and ``commonsense'' to render them acceptable. This social intelligence or ``commonsense'' of the robot is what eventually determines its social acceptability in the long run.Commonsense, however, is not that common. Robots can, thus, only learn to be acceptable with experience. However, teaching a humanoid the subtleties of a social interaction is not evident. Even a standard dialogue exchange integrates the widest possible panel of signs which intervene in the communication and are difficult to codify (synchronization between the expression of the body, the face, the tone of the voice, etc.). In such a scenario, learning the behavioral model of the robot is a promising approach. This learning can be performed with the help of AI techniques. This study tries to solve the problem of learning robot behavioral models in the Automated Planning and Scheduling (APS) paradigm of AI. In the domain of Automated Planning and Scheduling (APS), intelligent agents by virtue require an action model (blueprints of actions whose interleaved executions effectuates transitions of the system state) in order to plan and solve real world problems. During the course of this thesis, we introduce two new learning systems which facilitate the learning of action models, and extend the scope of these new systems to learn robot behavioral models. These techniques can be classified into the categories of non-optimal and optimal. Non-optimal techniques are more classical in the domain, have been worked upon for years, and are symbolic in nature. However, they have their share of quirks, resulting in a less-than-desired learning rate. The optimal techniques are pivoted on the recent advances in deep learning, in particular the Long Short Term Memory (LSTM) family of recurrent neural networks. These techniques are more cutting edge by virtue, and produce higher learning rates as well. This study brings into the limelight these two aforementioned techniques which are tested on AI benchmarks to evaluate their prowess. They are then applied to HRI traces to estimate the quality of the learnt robot behavioral model. This is in the interest of a long term objective to introduce behavioral autonomy in robots, such that they can communicate autonomously with humans without the need of ``wizard'' intervention.