Enhancing the Sense of Presence in Virtual Reality.

The vision of virtual reality, since Ivan Sutherland's first head mounted device in 1968, has been the re-creation of reality, something indistinguishable from reality, akin to what was depicted in the 1999 film, The Matrix. However, researchers and developers have largely favored visual perception over other senses, leading to the design of virtual worlds that perhaps look real but do not feel real. This favoring of the visual sense and more recently visual and audio senses, overlooks theory in psychology and phenomenology that places embodied action at the center of perception. That is to say, it is the virtual environment's ability to support and enable user actions that impacts perception and perhaps by extension, the user's sense of presence, not just the visual fidelity. Starting with Gibson's approach to action-based perception, we proposed a 4-D framework for creating virtual reality experiences that seamlessly blend extrinsic elements such as the user's real world context with intrinsic elements such as the hardware specifications, application, and interactive content, with the goal to enable a higher sense of presence.

SRI-EEG: State-Based Recurrent Imputation for EEG Artifact Correction.

Electroencephalogram (EEG) signals are often used as an input modality for Brain Computer Interfaces (BCIs). While EEG signals can be beneficial for numerous types of interaction scenarios in the real world, high levels of noise limits their usage to strictly noise-controlled environments such as a research laboratory. Even in a controlled environment, EEG is susceptible to noise, particularly from user motion, making it highly challenging to use EEG, and consequently BCI, as a ubiquitous user interaction modality. In this work, we address the EEG noise/artifact correction problem. Our goal is to detect physiological artifacts in EEG signal and automatically replace the detected artifacts with imputed values to enable robust EEG sensing overall requiring significantly reduced manual effort than is usual. We present a novel EEG state-based imputation model built upon a recurrent neural network, which we call SRI-EEG, and evaluate the proposed method on three publicly available EEG datasets. From quantitative and qualitative comparisons with six conventional and neural network based approaches, we demonstrate that our method achieves comparable performance to the state-of-the-art methods on the EEG artifact correction task.

Situated VR: Toward a Congruent Hybrid Reality Without Experiential Artifacts.

The vision of extended reality (XR) systems is living in a world where real and virtual elements seamlessly and contextually augment experiences of ourselves and the worlds we inhabit. While this integration promises exciting opportunities for the future of XR, it comes with the risk of experiential distortions and feelings of dissociation, especially related to virtual reality (VR). When transitioning from a virtual world to the real world, users report of experiential structures that linger on, as sort of after images, causing disruptions in their daily life. In this work, we define these atypical experiences as experiential artifacts (EAs) and present preliminary results from an informal survey conducted online with 76 VR users to highlight different types of artifacts and their durations. To avoid disruptions caused by these artifacts and simultaneously increase the user's sense of presence, we propose the idea of situated VR, which blends the real and virtual in novel ways that can reduce incongruencies between the two worlds. We discuss the implications of EAs, and through examples from our own work in building hybrid experiences, we demonstrate the potential and relevance of situated VR in the design of a future, more immersive, artifact-free hybrid reality.

Oasis: Procedurally Generated Social Virtual Spaces from 3D Scanned Real Spaces.

We present Oasis, a novel system for automatically generating immersive and interactive virtual reality environments for single and multiuser experiences. Oasis enables real-walking in the generated virtual environment by capturing indoor scenes in 3D and mapping walkable areas. It makes use of available depth information for recognizing objects in the real environment which are paired with virtual counterparts to leverage the physicality of the real world, for a more immersive virtual experience. Oasis allows co-located and remotely located users to interact seamlessly and walk naturally in a shared virtual environment. Experiencing virtual reality with currently available devices can be cumbersome due to presence of objects and furniture which need to be removed every time the user wishes to use VR. Our approach is new, in that it allows casual users to easily create virtual reality environments in any indoor space without rearranging furniture or requiring specialized equipment, skill or training. We demonstrate our approach to overlay a virtual environment over an existing physical space through fully working single and multiuser systems implemented on a Tango tablet device.