In this special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG), we are pleased to present the journal papers from the 24th IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2025), which will be held between October 8 and 12, 2025, in Daejeon, South Korea. ISMAR continues the over twenty-year-long tradition of IWAR, ISMR, and ISAR, and is the world's premier conference for Mixed and Augmented Reality.

Extended Reality (XR) interfaces offer engaging user experiences, but their effective design requires a nuanced understanding of user behavior and preferences. This knowledge is challenging to obtain without the widespread adoption of XR devices. We introduce MineXR, a design mining workflow and data analysis platform for collecting and analyzing personalized XR user interaction and experience data. MineXR enables elicitation of personalized interfaces from participants of a data collection: for any particular context, participants create interface elements using application screenshots from their own smartphone, place them in the environment, and simultaneously preview the resulting XR layout on a headset. Using MineXR, we contribute a dataset of personalized XR interfaces collected from 31 participants, consisting of 695 XR widgets created from 178 unique applications. We provide insights for XR widget functionalities, categories, clusters, UI element types, and placement. Our open-source tools and data support researchers and designers in developing future XR interfaces.

Emotion is dynamic. Changes in emotion can be hard to process during face-to-face interaction, yet transferring them into a shared virtual space becomes more challenging. This research first explores nine visual representations to amplify emotions in a virtual space, leading to a bi-directional emotion-sharing system (FeelMoji i/o). The second study investigates the effect of explicit emotion-sharing in interpersonal trust and task collaboration through three conditions - verbal only, verbal+positive visual, and verbal+honest visual using FeelMoji through the proposal of a framework of four factors (usability, integrity, behaviour, and collaboration). The results indicate that FeelMoji yields frequent emotion consensus as task milestones and positive interdependent behaviours between collaborators, which help develop conversations, affirm decision-making, and build familiarity and trust between strangers. Moreover, we discuss how our study can inspire future investigation in human-AI agent behaviours and large-scale multi-user virtual environments.

Augmented reality (AR) offers new ways to visualize information on-the-go. As noted in related work, AR graphics presented via optical see-through AR displays are particularly prone to color blending, whereby intended graphic colors may be perceptually altered by real-world backgrounds, ultimately degrading usability. This work adds to this body of knowledge by presenting a methodology for assessing AR interface color robustness, as quantitatively measured via shifts in the CIE color space, and qualitatively assessed in terms of users' perceived color name. We conducted a human factors study where twelve participants examined eight AR colors atop three real-world backgrounds as viewed through an in-vehicle AR head-up display (HUD); a type of optical see-through display used to project driving-related information atop the forward-looking road scene. Participants completed visual search tasks, matched the perceived AR HUD color against the WCS color palette, and verbally named the perceived color. We present analysis that suggests blue, green, and yellow AR colors are relatively robust, while red and brown are not, and discuss the impact of chromaticity shift and dispersion on outdoor AR interface design. While this work presents a case study in transportation, the methodology is applicable to a wide range of AR displays in many application domains and settings.

Technological advances in Head-Up Displays (HUDs) have renewed vehicle manufacturer interest. Recent research links time with eyes off of the road to increased chance of accidents, a problem that could be diminished when using HUDs. Before HUDs can be safely integrated, there must be proven methods of assessing these displays. NHTSA established guidelines for assessing in-vehicle tasks; however, these have not been examined with HUDs, differ from traditional Head-Down Displays (HDDs). This study followed most of the NHTSA guidelines for the Eye Glance Test for a visual text search task while tracking driving and secondary task performance measures. The HUD performed worse on the NHTSA eye glance test than the HDD did; however, the driving performance measures were superior when driving with the HUD. There were no significant differences in the secondary task performance between the two displays. Therefore, the NHTSA standard may not adequately assess HUDs in vehicles.

This study explored the effects that augmented reality graphics have on a drivers' distance estimation/depth perception in an augmented side view mirror. The study was conducted both inside a simulator and outside in a parking lot. Sixteen participants partook in the study, 8 in the simulator and 8 in the test vehicle outside. Distance judgments were compared across four side view mirror conditions both simulator and outdoor scenarios. Results show some opportunities for AR to improve depth judgments, but further analysis is necessary.

This study investigated the differences between head-up display (HUD) and head-down display (HDD) systems in vehicles with the ultimate goal of increasing driver performance and safety. Our initial focus was on comparing drivers' visual task performance (and associated driving behavior) while using a HUD and HDD. In a medium-fidelity driving simulator, 16 experienced drivers performed two types of tasks, a structured (text) and a semi-structured (grid) visual search task while driving. They first completed a baseline drive using a driving simulator with no secondary tasks followed by four drives under various conditions: HUD-Text, HUD-Grid, HDD-Text, and HDD-Grid. The study returned mixed results. HUDs generally contributed to more secondary task errors, but overall, users still preferred to use HUDs over HDDs. The response time to complete tasks was unevenly affected by display type. While there was no difference between response times in the grid tasks (HDD vs. HUD), the HUD was associated with significantly faster task performance as compared to the text tasks. These results suggest that HUDs may offer advantages to drivers in some task scenarios but more research is needed to understand under what conditions they do not add value.

Recent research indicates that users consistently underestimate depth judgments to Augmented Reality (AR) graphics when viewed through optical see-through displays. However, to our knowledge, little work has examined how AR graphics may affect depth judgments of real world objects that have been overlaid or annotated with AR graphics. This study explores whether AR graphics have directional effects on users' depth perception of real-world objects. Participants were shown an initial target location and were then asked to indicate when a moving target was perceived to be at the previously specified target location. Each participant experienced three different display conditions: no AR visual display (control), a conformal AR graphic overlaid on the pedestrian via head-up display (HUD), and the same graphic presented on a tablet physically located on the pedestrian. The judged distance from the correct location was recorded, and after each trial, participants' confidence in determining the correct distance was captured. Across all conditions, participants underestimated the distance of the physical object. Greater variability was observed in the accuracy of distance judgments under the AR HUD condition relative to the other two display conditions. In addition, participant confidence levels were significantly lower in the AR HUD condition.

Recent research indicates that users consistently underestimate depth judgments to Augmented Reality (AR) graphics when viewed through optical see-through displays. However, to our knowledge, little work has examined how AR graphics may affect depth judgments of real world objects that have been overlaid or annotated with AR graphics. This study begins a preliminary analysis whether AR graphics have directional effects on users' depth perception of real-world objects, as might be experienced in vehicle driving scenarios (e.g., as viewed via an optical see-through head-up display or HUD). Twenty-four participants were asked to judge the depth of a physical pedestrian proxy figure moving towards them at a constant rate of 1 meter/second. Participants were shown an initial target location that varied in distance from 11 to 20 m and were then asked to press a button to indicate when the moving target was perceived to be at the previously specified target location. Each participant experienced three different display conditions: no AR visual display (control), a conformal AR graphic overlaid on the pedestrian via a HUD, and the same graphic presented on a tablet physically located on the pedestrian. Participants completed 10 trials (one for each target distance between 11 and 20 inclusive) per display condition for a total of 30 trials per participant. The judged distance from the correct location was recorded, and after each trial, participants' confidence in determining the correct distance was captured. Across all conditions, participants underestimated the distance of the physical object consistent with existing literature. Greater variability was observed in the accuracy of distance judgments under the AR HUD condition relative to the other two display conditions. In addition, participant confidence levels were considerably lower in the AR HUD condition.