International audience

Climate change education often struggles to connect personal actions with environmental consequences. Meltdown is an immersive VR escape room that teaches sustainable food consumption and waste practices through scenario-based tasks and consequence-driven feedback. A user study (N = 36) found significant gains in familiarity, confidence, and behavioral intentions, with modest knowledge improvements. Exploratory metrics (n = 13) showed high accuracy on familiar decisions but lower accuracy on less intuitive ones. These findings suggest that consequence-driven VR can effectively engage learners, link everyday choices to visible outcomes, and foster sustainable behavior change.

Optical Head-Mounted Displays (OHMDs) allow users to read digital content while walking. A better understanding of how users allocate attention between these two tasks is crucial for improving OHMD interfaces. This paper introduces a computational model for simulating users’ attention switches between reading and walking. We model users’ decision to deploy visual attention as a hierarchical reinforcement learning problem, wherein a supervisory controller optimizes attention allocation while considering both reading activity and walking safety. Our model simulates the control of eye movements and locomotion as an adaptation to the given task priority, design of digital content, and walking speed. The model replicates key multitasking behaviors during OHMD reading while walking, including attention switches, changes in reading and walking speeds, and reading resumptions.

The rise of multitasking in contemporary lifestyles has positioned audio-first content as an essential medium for information consumption. We present AudioXtend, an approach to augment audiobook experiences during daily tasks by integrating glanceable, AI-generated visuals through optical see-through head-mounted displays (OHMDs). Our initial study showed that these visual augmentations not only preserved users’ primary task efficiency but also dramatically enhanced immediate auditory content recall by 33.3% and 7-day recall by 32.7%, alongside a marked improvement in narrative engagement. Through participatory design workshops involving digital arts designers, we crafted a set of design principles for visual augmentations that are attuned to the requirements of multitaskers. Finally, a 3-day take-home field study further revealed new insights for everyday use, underscoring the potential of assisted reality (aR) to enhance heads-up listening and incidental learning experiences.

It is essentially required to predict the ball’s flight trajectory accurately and timely for a robotic table tennis ball bouncing task. Existing solutions, which can be categorized into model-based and learning-based groups, both exhibits unpleasant disadvantages. For example, they often require to identify many dynamic parameters accurately or to collect extensive labeled data, which are generally very difficult or costly to achieve in real world. In this paper, we proposed a light-wight and rapid trajectory prediction approach for online table tennis bouncing tasks based on a simplified model. In the proposed approach, the ball’s flight poses are captured and estimated by a low-cost RGB-D camera. Then the ball’s landing position is predicted in advance by using a fitted 3D parabola. Compared with existing solutions, our proposed approach is lightweight and easy to deploy. In experiments, 66 flight trajectories of the ball are collected to serve as benchmark. The prediction errors for all landing positions are all less than 20mm, in which most of them are less than 10mm. In addition, the prediction can be achieved 141.7ms in advance, which is fast enough for the robotic arm to plan and move itself to the predicted landing point.

Modelling human visual attention is of great importance in the field of computer vision and has been widely explored for 3D imaging. Yet, in the absence of ground truth data, it is unclear whether such predictions are in alignment with the actual human viewing behavior in virtual reality environments. In this study, we work towards solving this problem by conducting an eye-tracking experiment in an immersive 3D scene that offers 6 degrees of freedom. A wide range of static point cloud models is inspected by human subjects, while their gaze is captured in real-time. The visual attention information is used to extract fixation density maps, that can be further exploited for saliency modelling. To obtain high quality fixation points, we devise a scheme that utilizes every recorded gaze measurement from the two eye-cameras of our set-up. The obtained fixation density maps together with the recorded gaze and head trajectories are made publicly available, to enrich visual saliency datasets for 3D models.