Teleportation has become the de facto standard of locomotion in Virtual Reality (VR) environments. However, teleportation with parabolic and linear target aiming methods is restricted to horizontal 2D planes and it is unknown how they transfer to the 3D space. In this paper, we propose six 3D teleportation methods in virtual environments based on the combination of two existing aiming methods (linear and parabolic) and three types of transitioning to a target (instant, interpolated and continuous). To investigate the performance of the proposed teleportation methods, we conducted a controlled lab experiment (N = 24) with a mid-air coin collection task to assess accuracy, efficiency and VR sickness. We discovered that the linear aiming method leads to faster and more accurate target selection. Moreover, a combination of linear aiming and instant transitioning leads to the highest efficiency and accuracy without increasing VR sickness.
Navigating large-scale virtual spaces is a major challenge in Virtual Reality (VR) applications due to real-world spatial limitations. Walking-in-place (WIP) locomotion solutions may provide a natural approach for VR use cases that require locomotion to share similar qualities with walking in real-life. However, there is limited knowledge on the range of experiences across common WIP methods to inform the design of usable WIP solutions using consumer-accessible components. This paper contributes to this knowledge via a user study with 40 participants that experienced several easy-to-setup WIP methods in a VR commuting simulation. A nuanced understanding of cybersickness and exertion relationships and walking affordances based on different tracker setups were among the findings derived from a corroborated analysis of think-aloud, interview, and observational data, supplemented with self-reports of VR sickness, presence and flow. Practical design insights were then constructed along the dimensions of cybersickness, affordances, space and user interfaces.
Teleportation, which instantly moves users from their current location to the target location, has become the most popular locomotion technique in VR games. It enables fast navigation with reduced VR sickness but results in significantly reduced immersion.
We present HeadWind, a novel approach to improve the experience of teleportation by simulating the haptic sensation of air drag when rapidly moving through the air in real life. Specifically, HeadWind modulates bursts of compressed air to the face and uses multiple nozzles to provide directional cues. To design the wearable device and to model airflow speed and duration for teleportation, we conducted three formative studies and a design session. User experience evaluation with 24 participants showed that HeadWind significantly improved realism, immersion, and enjoyment of teleportation in VR (p<.01) with large effect sizes (r>0.5), and was preferred by 96% of participants.
Esports play can cultivate real world skills. However, the path to mastery is not easy, and difficulty progressing can result in discontinuation. In the absence of a human coach, computational tools may provide much needed guidance. However, the specific improvement activities that players engage in and the exact challenges they face are not well defined in the context of computational support. As such, most tools can only support players based on a high level understanding of their practices. We present the results of an interview study (n=17) that identified four improvement activities: practicing, leveraging the knowledge of others, tracking performance, and reflecting on gameplay and setting goals, and four challenges: coordinating and collaborating with teammates, knowing what to do next, tracking game state, and tracking skill and improvement. We discuss six implications for future design and development based on these results.
In this study, we analyzed how the performance of professional Go players has changed since the advent of AlphaGo, the first artificial intelligence (AI) application to defeat a human world Go champion. We interviewed and surveyed professional Go players and found that AI has been actively introduced into the Go training process since the advent of AlphaGo. The significant impact of AI-based training was confirmed in a subsequent analysis of 6,292 games in Korean Go tournaments and Elo rating data of 1,362 Go players worldwide. Overall, the tendency of players to make moves similar to those recommended by AI has sharply increased since 2017. The degree to which players’ expected win rates fluctuate during a game has also decreased significantly since 2017. We also found that AI-based training has provided more benefits to senior players and allowed them to achieve Elo ratings higher than those of junior players.