This paper introduces Sketched Reality, an approach that com- bines AR sketching and actuated tangible user interfaces (TUI) for bi-directional sketching interaction. Bi-directional sketching enables virtual sketches and physical objects to “affect” each other through physical actuation and digital computation. In the exist- ing AR sketching, the relationship between virtual and physical worlds is only one-directional — while physical interaction can affect virtual sketches, virtual sketches have no return effect on the physical objects or environment. In contrast, bi-directional sketch- ing interaction allows the seamless coupling between sketches and actuated TUIs. In this paper, we employ tabletop-size small robots (Sony Toio) and an iPad-based AR sketching tool to demonstrate the concept. In our system, virtual sketches drawn and simulated on an iPad (e.g., lines, walls, pendulums, and springs) can move, actuate, collide, and constrain physical Toio robots, as if virtual sketches and the physical objects exist in the same space through seamless coupling between AR and robot motion. This paper contributes a set of novel interactions and a design space of bi-directional AR sketching. We demonstrate a series of potential applications, such as tangible physics education, explorable mechanism, tangible gaming for children, and in-situ robot programming via sketching.
We present PassengXR, an open-source toolkit for creating passenger eXtended Reality (XR) experiences in Unity. XR allows travellers to move beyond the physical limitations of in-vehicle displays, rendering immersive virtual content based on - or ignoring - vehicle motion. There are considerable technical challenges to using headsets in moving environments: maintaining the forward bearing of IMU-based headsets; conflicts between optical and inertial tracking of inside-out headsets; obtaining vehicle telemetry; and the high cost of design given the necessity of testing in-car. As a consequence, existing vehicular XR research typically relies on controlled, simple routes to compensate. PassengXR is a cost-effective open-source in-car passenger XR solution. We provide a reference set of COTS hardware that enables the broadcasting of vehicle telemetry to multiple headsets. Our software toolkit then provides support to correct vehicle-headset alignment, and then create a variety of passenger XR experiences, including: vehicle-locked content; motion- and location-based content; and co-located multi-passenger applications. PassengXR also supports the recording and playback of vehicle telemetry, assisting offline design without resorting to costly in-car testing. Through an evaluation-by-demonstration, we show how our platform can assist practitioners in producing novel, multi-user passenger XR experiences.
Gaze-based target suffers from low input precision and target occlusion. In this paper, we explored to leverage the continuous eyelid movement to support high-efficient and occlusion-robust dwell-based gaze pointing in virtual reality. We first conducted two user studies to examine the users' eyelid movement pattern both in unintentional and intentional conditions. The results proved the feasibility of leveraging intentional eyelid movement that was distinguishable with natural movements for input. We also tested the participants' dwelling pattern for targets with different sizes and locations. Based on these results, we propose DEEP, a novel technique that enables the users to see through occlusions by controlling the aperture angle of their eyelids and dwell to select the targets with the help of a probabilistic input prediction model. Evaluation results showed that DEEP with dynamic depth and location selection incorporation significantly outperformed its static variants, as well as a naive dwelling baseline technique. Even for 100% occluded targets, it could achieve an average selection speed of 2.5s with an error rate of 2.3%.
Freehand interactions with augmented and virtual reality are grow- ing in popularity, but they lack reliability and robustness. Implicit behavior from users, such as hand or gaze movements, might pro- vide additional signals to improve the reliability of input. In this paper, the primary goal is to improve the detection of a selection gesture in VR during point-and-click interaction. Thus, we propose and investigate the use of information contained within the hand motion dynamics that precede a selection gesture. We built two models that classified if a user is likely to perform a selection gesture at the current moment in time. We collected data during a pointing-and-selection task from 15 participants and trained two models with different architectures, i.e., a logistic regression classifier was trained using predefined hand motion features and a temporal convolutional network (TCN) classifier was trained using raw hand motion data. Leave-one-subject-out cross-validation PR- AUCs of 0.36 and 0.90 were obtained for each model respectively, demonstrating that the models performed well above chance (=0.13). The TCN model was found to improve the precision of a noisy selection gesture by 11.2% without sacrificing recall performance. An initial analysis of the generalizability of the models demonstrated above-chance performance, suggesting that this approach could be scaled to other interaction tasks in the future.
With the proliferation of consumer-level virtual reality (VR) devices, users started experiencing VR in less controlled environments, such as in social gatherings and public areas. While the current VR hardware provides an increasingly immersive experience, it ignores stimuli originating from the physical surroundings that distract users from the VR experience. To block distractions from the outside world, many users wear noise-canceling headphones. However, this is insufficient to block loud or transient sounds (e.g., drilling or hammering) and, especially, multi-modal distractions (e.g., air drafts, temperature shifts from an A/C, construction vibrations, or food smells). To tackle this, we explore a new concept, where we directly integrate the distracting stimuli from the user’s physical surroundings into their virtual reality experience to enhance presence. Using our approach, an otherwise distracting wind gust can be directly mapped to the sway of trees in a VR experience that already contains trees. Using our novel approach, we demonstrate how to integrate a range of distractive stimuli into the VR experience, such as haptics (temperature, vibrations, touch), sounds, and smells. To validate our approach, we conducted three user studies and a technical evaluation. First, to validate our key principle, we conducted a controlled study where participants were exposed to distractions while playing a VR game. We found that our approach improved users’ sense of presence, compared to wearing noise-canceling headphones. From these results, we engineered a sensing module that detects a set of simple distractive signals (e.g., sounds, winds, and temperature shifts). We validated our hardware in a technical evaluation and in an out-of-lab study where participants played VR games in an uncontrolled environment. Moreover, to gather the perspective of VR content creators that might one day utilize a system inspired by our findings, we invited game designers to use our approach and collected their feedback and VR designs. Finally, we present design considerations for mapping distracting external stimuli and discuss ethical considerations of integrating real-world stimuli into virtual reality.
Intelligent suggestion techniques can enable low-friction selection-based input within virtual or augmented reality (VR/AR) systems. Such techniques leverage probability estimates from a target prediction model to provide users with an easy-to-use method to select the most probable target in an environment. For example, a system could highlight the predicted target and enable a user to select it with a simple click. However, as the probability estimates can be made at any time, it is unclear when an intelligent suggestion should be presented. Earlier suggestions could save a user time and effort but be less accurate. Later suggestions, on the other hand, could be more accurate but save less time and effort. This paper thus proposes a computational framework that can be used to determine the optimal timing of intelligent suggestions based on user-centric costs and benefits. A series of studies demonstrated the value of the framework for minimizing task completion time and maximizing suggestion usage and showed that it was both theoretically and empirically effective at determining the optimal timing for intelligent suggestions.