A near-regular texture (NRT) is a geometric and photometric deformation from its regular origin - a congruent wallpaper pattern formed by 2D translations of a single tile. A dynamic NRT is an NRT under motion. Although NRTs are pervasive in man-made and natural environments, effective computational algorithms for NRTs are few. This paper addresses specific computational challenges in modeling and tracking dynamic NRTs, including ambiguous correspondences, occlusions, and drastic illumination and appearance variations. We propose a lattice-based Markov-random-field (MRF) model for dynamic NRTs in a 3D spatiotemporal space. Our model consists of a global lattice structure that characterizes the topological constraint among multiple textons and an image observation model that handles local geometry and appearance variations. Based on the proposed MRF model, we develop a tracking algorithm that utilizes belief propagation and particle filtering to effectively handle the special challenges of the dynamic NRT tracking without any assumption on the motion types or lighting conditions. We provide quantitative evaluations of the proposed method against existing tracking algorithms and demonstrate its applications in video editing

Cybersickness (CS) is one of the challenges that has hindered the widespread adoption of Virtual Reality and its applications. Consequently, a number of studies have focused on extensively understanding and reducing CS. Inspired by previous work that has sought to reduce CS using foveated rendering and Field of View (FOV) restrictions, we investigated how the presence and size of a static central window in peripheral FOV blurring affects CS. To facilitate this peripheral FOV blur, we applied a Gaussian blur effect in the display peripheral region, provisioning a full-resolution central window. Thirty participants took part in a three-session, within-subjects experiment, performing search and spatial updating tasks in a first-person, slow-walking, maze-traveling scenario. Two different central window sizes (small and large) were tested against a baseline condition that didn’t feature display peripheral blurring. Results revealed that the baseline condition produced higher levels of CS than both conditions with a central window. While there were no significant differences between the small and large windows, we observed interaction effects suggesting an influence of window size on “adaptation to CS.” When the central window is small, adaptation to CS seems to take more time but is more pronounced. The interventions had no effect on spatial updating and presence, but were detectable when the blurred area was larger (small central window). Lower sickness levels observed in both window conditions supports the use of peripheral FOV blurring to reduce CS, reducing our dependence on eye tracking. This being said, researchers must strive to find the right balance between window size and detectability to ensure seamless virtual experiences.

Cybersickness (CS) is one of the challenges that has hindered the widespread adoption of Virtual Reality (VR). Consequently, researchers continue to explore novel means to mitigate the undesirable effects associated with this affliction, one that may require a combination of remedies as opposed to a solitary stratagem. Inspired by research probing into the use of distractions as a means to control pain, we investigated the efficacy of this countermeasure against CS, studying how the introduction of temporally time-gated distractions affects this malady during a virtual experience featuring active exploration. Downstream of this, we discuss how other aspects of the VR experience are affected by this intervention. We discuss the results of a between-subjects study manipulating the presence, sensory modality, and nature of periodic and short-lived (5-12 seconds) distractor stimuli across 4 experimental conditions: (1) no-distractors (ND); (2) auditory distractors (AD); (3) visual distractors (VD); (4) cognitive distractors (CD). Two of these conditions (VD and AD) formed a yoked control design wherein every matched pair of ‘seers’ and ‘hearers’ was periodically exposed to distractors that were identical in terms of content, temporality, duration, and sequence. In the CD condition, each participant had to periodically perform a 2-back working memory task, the duration and temporality of which was matched to distractors presented in each matched pair of the yoked conditions. These three conditions were compared to a baseline control group featuring no distractions. Results indicated that the reported sickness levels were lower in all three distraction groups in comparison to the control group. The intervention was also able to both increase the amount of time users were able to endure the VR simulation, as well as avoid causing detriments to spatial memory and virtual travel efficiency. Overall, it appears that it may be possible to make users less consciously aware and bothered by the symptoms of CS, thereby reducing its perceived severity.

User representations are critical to the virtual experience, and involve both the input device used to support interactions as well as how the user is virtually represented in the scene. Inspired by previous work that has shown effects of user representations on the perceptions of relatively static affordances, we attempt to investigate how end-effector representations affect the perceptions of affordances that dynamically change over time. Towards this end, we empirically evaluated how different virtual hand representations affect users' perceptions of dynamic affordances in an object retrieval task wherein users were tasked with retrieving a target from a box for a number of trials while avoiding collisions with its moving doors. We employed a 3 (virtual end-effector representation) X 13 (frequency of moving doors) X 2 (target object size) multi-factorial design, manipulating the input modality and its concomitant virtual end-effector representation as a between-subjects factor across three experimental conditions: (1) Controller (using a controller represented as a virtual controller); (2) Controller-hand (using a controller represented as a virtual hand); (3) Glove (using a hand tracked hi-fidelity glove represented as a virtual hand). Results indicated that the controller-hand condition produced lower levels of performance than both the other conditions. Furthermore, users in this condition exhibited a diminished ability to calibrate their performance over trials. Overall, we find that representing the end-effector as a hand tends to increase embodiment but can also come at the cost of performance, or an increased workload due to a discordant mapping between the virtual representation and the input modality used. It follows that VR system designers should carefully consider the priorities and target requirements of the application being developed when choosing the type of end-effector representation for users to embody in immersive virtual experiences.

Substantial developments have been established in the past few years for enhancing the performance of brain–computer interface (BCI) based on steady-state visual evoked potential (SSVEP). The past SSVEP-BCI studies utilized different target frequencies with flashing stimuli in many different applications. However, it is not easy to recognize user’s mental state changes when performing the SSVEP-BCI task. What we could observe was the increasing EEG power of the target frequency from the user’s visual area. BCI user’s cognitive state changes, especially in mental focus state or lost-in-thought state, will affect the BCI performance in sustained usage of SSVEP. Therefore, how to differentiate BCI users’ physiological state through exploring their neural activities changes while performing SSVEP is a key technology for enhancing the BCI performance. In this study, we designed a new BCI experiment which combined working memory task into the flashing targets of SSVEP task using 12 Hz or 30 Hz frequencies. Through exploring the EEG activity changes corresponding to the working memory and SSVEP task performance, we can recognize if the user’s cognitive state is in mental focus or lost-in-thought. Experiment results show that the delta (1–4 Hz), theta (4–7 Hz), and beta (13–30 Hz) EEG activities increased more in mental focus than in lost-in-thought state at the frontal lobe. In addition, the powers of the delta (1–4 Hz), alpha (8–12 Hz), and beta (13–30 Hz) bands increased more in mental focus in comparison with the lost-in-thought state at the occipital lobe. In addition, the average classification performance across subjects for the KNN and the Bayesian network classifiers were observed as 77% to 80%. These results show how mental state changes affect the performance of BCI users. In this work, we developed a new scenario to recognize the user’s cognitive state during performing BCI tasks. These findings can be used as the novel neural markers in future BCI developments.