Redirected walking (RDW) allows people to explore large virtual environments while walking within a smaller physical space. When physical space is limited, explicit resets such as turn-in-place are unavoidable. Previous studies have reduced the frequency of resets by adjusting user paths. However, resets remain necessary under severe spatial constraints. The frequent use of turn-in-place resets interrupts locomotion and can degrade task performance and user experience. We present Non-Euclidean Portal Reset (NEPR), a reset technique that enables continuous experiences without pausing the user's walking. When a collision risk is detected, NEPR opens a virtual portal leading to a short, non-Euclidean corridor. Traversing the corridor repositions and reorients the user. The exit returns the user near the point of interest target in the primary world, maintaining flow. To evaluate the effectiveness of NEPR, we conducted user experiments comparing (1) the conventional turn-in-place reset, (2) NEPR, and (3) a hybrid method combining both approaches. Our results demonstrate that NEPR and the combined technique significantly improve the user experience and task performance compared to the traditional method. Overall, NEPR reframes resets as seamless transitions rather than interruptions, enhancing the practicality of RDW.

Eye gaze is regarded as a promising interaction modality in extended reality (XR) environments. However, to address the challenges posed by the Midas touch problem, the determination of selection intention frequently relies on the implementation of additional manual selection techniques, such as explicit gestures (e.g., controller/hand inputs or dwell), which are inherently limited in their functionality. We hereby present a machine learning (ML) model based on the Bayesian framework, which is employed to predict user selection intention in real-time, with the unique distinction that all data used for training and prediction are obtained from gaze data alone. The model utilizes a Bayesian approach to transform gaze data into selection probabilities, which are subsequently fed into an ML model to discern selection intentions. In Study 1, a high-performance model was constructed, enabling real-time inference using solely gaze data. This approach was found to enhance performance, thereby validating the efficacy of the proposed methodology. In Study 2, a user study was conducted to validate a manual-free technique based on the prediction model. The advantages of eliminating explicit gestures and potential applications were also discussed.