Young adults often take breaks from screen-intensive work by consuming digital content on mobile phones, which undermines rest through visual fatigue and inactivity. We introduce a design framework that embeds light break activities into media content on AR smart glasses, balancing engagement and recovery, which employs three strategies: (1) seamlessly guiding users by embedding activity cues aligned with media elements; (2) transitioning to audio-centric formats to reduce visual load while sustaining immersion; and (3) structuring sessions with "rise-peak-closure"pacing for smooth transitions. In a within-subjects study (N=16) comparing passive viewing, reminder-based breaks, and non-narrative activities, InteractiveBreak instantiated from our framework seamlessly guided activities, sustained engagement, and enhanced break quality. These findings demonstrate wearable AR's potential to support restorative relaxation by transforming breaks into engaging, meaningful experiences. © 2026 the owner/author(s).

The growing availability of time-varying spatial 3D (S4D) data, such as ocean and atmospheric datasets, has created opportunities for studying dynamic phenomena across time and 3D space. However, designing effective visualizations for S4D data remains challenging due to the high cognitive demands and complexity of these datasets. While techniques like animation and small multiples have been applied in Virtual Reality (VR) and desktop environments, the lack of understanding of analysts’ tasks and challenges limits the development of better visualization techniques. To fill this gap, we conducted an empirical study with domain experts across various fields, comparing four visualization techniques: VR animation, VR small multiples, desktop animation, and desktop small multiples. We identified the strengths and weaknesses of the four techniques, as well as key analytical tasks, current practices, and challenges in S4D data analysis. Finally, we outlined future research opportunities for advancing S4D visualization techniques.

Dyslexia is a common neurobiological learning disorder significantly impacting reading, writing, and spelling worldwide. Early identification and intervention are essential, but most pre-screening tools focus on Latin languages, leaving Chinese-speaking students underserved. To address this gap, we conduct semi-structured interviews with special education (special-ed) teachers to gather their needs for dyslexia pre-screening tailored to Chinese contexts. Using their insights, we have developed DysVis, a user-centered data visualization system that combines handwriting analysis, body movement keypoint conversion, and a comprehensive visualization interface. DysVis provides teachers with multi-level visualizations, such as performance overviews, task analyses, handwriting observations, and behavioural insights, enabling them to identify the root causes of learning difficulties. Our evaluations, including case studies, a user study, and expert interviews, demonstrate that DysVis is user-friendly and effective in quickly identifying at-risk students, ultimately enhancing learning outcomes for Chinese-speaking students with dyslexia.

Animated virtual reality (VR) stories, combining the presence of VR and the artistry of computer animation, offer a compelling way to deliver messages and evoke emotions. Motivated by the growing demand for immersive narrative experiences, more creators are creating animated VR stories. However, a holistic understanding of their creation processes and challenges involved in crafting these stories is still limited. Based on semi-structured interviews with 21 animated VR story creators, we identify ten common stages in their end-to-end creation processes, ranging from idea generation to evaluation, which form diverse workflows that are story-driven or visual-driven. Additionally, we highlight nine unique issues that arise during the creation process, such as a lack of reference material for multi-element plots, the absence of specific functionalities for story integration, and inadequate support for audience evaluation. We compare the creation of animated VR stories to general XR applications and distill several future research opportunities.

The increasing popularity of Virtual Reality (VR) has provided a new medium for narrating spatiotemporal data stories. Compared to traditional 2D environments, telling spatiotemporal stories in VR holds the promise of offering a more immersive and engaging experience for audiences. However, when creating VR spatiotemporal data stories, story creators may feel overwhelmed by the numerous narrative elements involved and there is a disconnect between the creation and viewing environments. To address these challenges, we designed a hierarchical narrative structure with three levels: Story Line, Story Piece, and Viewpoint. We then introduce ST2VR, an interactive authoring system that supports the creation of spatiotemporal data stories within an immersive environment. The system features two types of interactive interfaces to support the organization of the overall narrative as well as the immersive design of story details. A use case demonstrates the process of authoring VR spatiotemporal data stories using ST2VR, and a user study evaluates the system’s efficiency and effectiveness.

Conversational LLMs have been widely adopted by domain users with limited programming experience to solve domain problems.However, these users often face misalignment between their intent and generated code, resulting in frustration and rounds of clarification.This work first investigates the cause of this misalignment, which dues to bidirectional ambiguity: both user intents and coding tasks are inherently nonlinear, yet must be expressed and interpreted through linear prompts and code sequences.To address this, we propose direct intent-task matching, a new human-LLM interaction paradigm that externalizes and enables direct manipulation of the LLM understanding, i.e., the coding tasks and their relationships inferred by the LLM prior to code generation.As a proof-of-concept, this paradigm is then implemented in NeuroSync, which employs a knowledge distillation pipeline to extract LLM understanding, user intents, and their mappings, and enhances the alignment by allowing users to intuitively inspect and edit them via visualizations.We evaluate the algorithmic components of NeuroSync via technical experiments, and assess its overall usability and effectiveness via a user study (N=12).The results show that it enhances intent-task alignment, lowers cognitive effort, and improves coding efficiency.

Figure 1: An overview of KinemaFX.KinemaFX supports interactive particle effect exploration and customization through three stages: (a) User Intent Input.Users can express their initial exploration intent by combining semantic input (a1) and graphical input (a2).(b) Effect Exploration.Kinematic supports particle effects searching based on controllable weights of semantic and kinematic similarity (b1).Users iteratively explore the space by selecting satisfying effects, thereby implicitly conveying their preferences(b2).(c) Effect Composition.From the explored particle effects, users can select (c1) and control individual effects' transformations and temporal features (c2) to compose effect artworks (c3).

With the rise of short-form video platforms and the increasing availability of data, we see the potential for people to share short-form videos embedded with data in situ (e.g., daily steps when running) to increase the credibility and expressiveness of their stories. However, creating and sharing such videos in situ is challenging since it involves multiple steps and skills (e.g., data visualization creation and video editing), especially for amateurs. By conducting a formative study (N=10) using three design probes, we collected the motivations and design requirements. We then built VisTellAR, a mobile AR authoring tool, to help amateur video creators embed data visualizations in short-form videos in situ. A two-day user study shows that participants (N=12) successfully created various videos with data visualizations in situ and they confirmed the ease of use and learning. AR pre-stage authoring was useful to assist people in setting up data visualizations in reality with more designs in camera movements and interaction with gestures and physical objects to storytelling.

The rising popularity of 360-degree images and virtual reality (VR) has spurred a growing interest among creators in producing visually appealing content through effective color grading processes. Although existing computational approaches have simplified the global color adjustment for entire images with Preferential Bayesian Optimization (PBO), they neglect local colors for points of interest and are not optimized for the immersive nature of VR. In response, we propose a dual-level PBO framework that integrates global and local color adjustments tailored for VR environments. We design and evaluate a novel context-aware preferential Gaussian Process (GP) to learn contextual preferences for local colors, taking into account the dynamic contexts of previously established global colors. Additionally, recognizing the limitations of desktop-based interfaces for comparing 360-degree images, we design three VR interfaces for color comparison. We conduct a controlled user study to investigate the effectiveness of the three VR interface designs and find that users prefer to be enveloped by one 360-degree image at a time and to compare two rather than four color-graded options.

Tour guidance in virtual museums encourages multi-modal interactions to boost user experiences, concerning engagement, immersion, and spatial awareness. Nevertheless, achieving the goal is challenging due to the complexity of comprehending diverse user needs and accommodating personalized user preferences. Informed by a formative study that characterizes guidance-seeking contexts, we establish a multi-modal interaction design framework for virtual tour guidance. We then design VirtuWander, a two-stage innovative system using domain-oriented large language models to transform user inquiries into diverse guidance-seeking contexts and facilitate multi-modal interactions. The feasibility and versatility of VirtuWander are demonstrated with virtual guiding examples that encompass various touring scenarios and cater to personalized preferences. We further evaluate VirtuWander through a user study within an immersive simulated museum. The results suggest that our system enhances engaging virtual tour experiences through personalized communication and knowledgeable assistance, indicating its potential for expanding into real-world scenarios.

We introduce AniCraft, a mixed reality system for prototyping 3D character animation using physical proxies crafted from everyday objects. Unlike existing methods that require specialized equipment to support the use of physical proxies, AniCraft only requires affordable markers, webcams, and daily accessible objects and materials. AniCraft allows creators to prototype character animations through three key stages: selection of virtual characters, fabrication of physical proxies, and manipulation of these proxies to animate the characters. This authoring workflow is underpinned by diverse physical proxies, manipulation types, and mapping strategies, which ease the process of posing virtual characters and mapping user interactions with physical proxies to animated movements of virtual characters. We provide a range of cases and potential applications to demonstrate how diverse physical proxies can inspire user creativity. User experiments show that our system can outperform traditional animation methods for rapid prototyping. Furthermore, we provide insights into the benefits and usage patterns of different materials, which lead to design implications for future research.

Reminiscing with photo collections offers significant psychological benefits but poses challenges for people with visual impairment (PVI). Their current reliance on sighted help restricts the flexibility of this activity. In response, we explored using a chatbot in a preliminary study. We identified two primary challenges that hinder effective reminiscence with a chatbot: the scattering of information and a lack of proactive guidance. To address these limitations, we present Memory Reviver, a proactive chatbot that helps PVI reminisce with a photo collection through natural language communication. Memory Reviver incorporates two novel features: (1) a Memory Tree, which uses a hierarchical structure to organize the information in a photo collection; and (2) a Proactive Strategy, which actively delivers information to users at proper conversation rounds. Evaluation with twelve PVI demonstrated that Memory Reviver effectively facilitated engaging reminiscence, enhanced understanding of photo collections, and delivered natural conversational experiences. Based on our findings, we distill implications for supporting photo reminiscence and designing chatbots for PVI.

This paper exploits ubiquitous desktop interaction data as an input source for generating virtual reality (VR) interaction data, which can benefit tasks like user behavior analysis and experience enhancement. Time-varying stroke gestures are selected as the primary focus because of their prevalence across various applications and their diverse patterns. The commonalities (e.g., features like velocity and curvature) between desktop and VR strokes allow the generation of additional dimensions (e.g., z vectors) in VR strokes. However, distribution shifts exist between different interaction environments (i.e., desktop vs. VR), and within the same interaction environment for different strokes by various users, making it challenging to build models capable of generalizing to unseen distributions. To address the challenges, we formulate the problem of generating VR strokes from desktop strokes as a conditional time series generation problem, aiming to learn representations that are capable of handling out-of-distribution data. We propose a novel architecture based on conditional generative adversarial networks, with the generator encompassing three steps: discretizing the output space, characterizing latent distributions, and learning conditional domain-invariant representations. We evaluate the effectiveness of our methods by comparing them with state-of-the-art time series generation models and conducting ablation studies. We further illustrate the applicability of the enriched VR datasets through two applications: VR stroke classification and stroke prediction.

As a famed Chinese classical garden, the Jichang Garden was a constant inspiration to many poets in its hundreds of years’ history, who composed a rich body of poems—a valuable intangible cultural heritage. While tourists tend to pay attention to tangible natural scenery and historical architectures, they often neglect intangible cultural heritage—poems. We interviewed 23 tourists and found that augmented reality (AR) was viable for tourists to enjoy the physical scenery and the poetry simultaneously. We developed an initial prototype of PoeticAR, which presents poems based on physical scenery to enhance tourists’ cultural and aesthetic experience. We further revised the prototype based on the ideas generated from a workshop with 18 tourists. We conducted a between-subject user study with 30 tourists to compare PoeticAR with Video. Results showed that PoeticAR significantly motivated tourists’ interest in poems, enhanced the cultural and aesthetic tour experience in Jichang Garden, and increased awareness of Intangible Cultural Heritage of Cultural Heritage sites.

This paper presents a design space of interaction techniques to engage with visualizations that are printed on paper and augmented through Augmented Reality. Paper sheets are widely used to deploy visualizations and provide a rich set of tangible affordances for interactions, such as touch, folding, tilting, or stacking. At the same time, augmented reality can dynamically update visualization content to provide commands such as pan, zoom, filter, or detail on demand. This paper is the first to provide a structured approach to mapping possible actions with the paper to interaction commands. This design space and the findings of a controlled user study have implications for future designs of augmented reality systems involving paper sheets and visualizations. Through workshops ( N=20) and ideation, we identified 81 interactions that we classify in three dimensions: 1) commands that can be supported by an interaction, 2) the specific parameters provided by an (inter)action with paper, and 3) the number of paper sheets involved in an interaction. We tested user preference and viability of 11 of these interactions with a prototype implementation in a controlled study ( N=12, HoloLens 2) and found that most of the interactions are intuitive and engaging to use. We summarized interactions (e.g., tilt to pan) that have strong affordance to complement "point" for data exploration, physical limitations and properties of paper as a medium, cases requiring redundancy and shortcuts, and other implications for design.

Given a large number of applications and complex processing procedures, how to efficiently shift and schedule tax officers to provide good services to taxpayers is now receiving more attention from tax authorities. The availability of historical application data makes it possible for tax managers to shift and schedule staff with data support, but it is unclear how to properly leverage the historical data. To investigate the problem, this study adopts a user-centered design approach. We first collect user requirements by conducting interviews with tax managers and characterize their requirements of shifting and scheduling into time series prediction and resource scheduling problems. Then, we propose Tax-Scheduler, an interactive visualization system with a time-series prediction algorithm and genetic algorithm to support staff shifting and scheduling in the tax scenarios. To evaluate the effectiveness of the system and understand how non-technical tax managers react to the system with advanced algorithms and visualizations, we conduct user interviews with tax managers and distill several implications for future system design.

This article presents a new approach based on deep learning to automatically extract colormaps from visualizations. After summarizing colors in an input visualization image as a Lab color histogram, we pass the histogram to a pre-trained deep neural network, which learns to predict the colormap that produces the visualization. To train the network, we create a new dataset of ∼ 64K visualizations that cover a wide variety of data distributions, chart types, and colormaps. The network adopts an atrous spatial pyramid pooling module to capture color features at multiple scales in the input color histograms. We then classify the predicted colormap as discrete or continuous, and refine the predicted colormap based on its color histogram. Quantitative comparisons to existing methods show the superior performance of our approach on both synthetic and real-world visualizations. We further demonstrate the utility of our method with two use cases, i.e., color transfer and color remapping.

When designing infographics, general users usually struggle with getting desired color palettes using existing infographic authoring tools, which sometimes sacrifice customizability, require design expertise, or neglect the influence of elements' spatial arrangement. We propose a data-driven method that provides flexibility by considering users' preferences, lowers the expertise barrier via automation, and tailors suggested palettes to the spatial layout of elements. We build a recommendation engine by utilizing deep learning techniques to characterize good color design practices from data, and further develop InfoColorizer, a tool that allows users to obtain color palettes for their infographics in an interactive and dynamic manner. To validate our method, we conducted a comprehensive four-part evaluation, including case studies, a controlled user study, a survey study, and an interview study. The results indicate that InfoColorizer can provide compelling palette recommendations with adequate flexibility, allowing users to effectively obtain high-quality color design for input infographics with low effort.

Temporal event sequences are becoming increasingly important in many application domains such as website click streams, user interaction logs, electronic health records and car service records. A real-world dataset with a large number of event sequences of varying lengths is complex and difficult to analyze. To support visual exploration of the data, it is desirable yet challenging to provide a concise and meaningful overview of sequences. In this paper, we focus on the stage, that is, a frequently occurring subsequence in the dataset. We introduce StageMap, a novel visualization technique to summarize event sequence data into a set of stage progression patterns. The resulting overview is more concise compared with event-level summarization and supports level-of-detail exploration. We further present a visual analytics system with four linked views, which are overview, tree view, stage view and sequences view. We also present case studies and discuss advantages and limitations of applying StageMap to real-world scenarios.