DevLan/vispubs huggingface dataset:
Visualising Pianists' Touch: Transcribing Expressive Piano Performance from Audio to Piano Key Motion
Authors: Jingjing Tang 0002, Shinichi Furuya, Hayato Nishioka, Momoko Shioki, Geraint A. Wiggins, George Fazekas, Vincent K. M. Cheung
Published at CHI, 2026

Detailed measurements of piano key motion capture touch, timing, and dynamic control, providing crucial performance insights. Such expressive gestures are overlooked in MIDI, which only records pitch onset, duration, and velocity. Here, we introduce a novel transcription technique that directly maps audio from expressive piano performance to continuous piano key motion. User studies reveal a preference to the transcribed key motion trajectories over MIDI in representing sound, and over 80% accuracy in matching transcribed trajectories to audio from contrasting piano expressions. Follow-up interviews further indicate that the visualised trajectories can reveal subtle performance nuances and provide actionable guidance for both teaching and practice. An interface example for pedagogy and performance analysis utilising our technique is also illustrated. By providing a physically grounded performance representation that musicians can interpret and act upon, this work establishes a foundation for future interactive tools in music pedagogy, performance feedback, and embodied musical learning.
Visualization of Tracking Uncertainty in AR-based Surgical Guidance
Authors: Chaymae Acherki, Laurence Nigay, Quentin Roy, Thibault Salque
Published at CHI, 2026

Uncertainty caused by instrument tracking errors affects critical tasks such as surgery assisted by Augmented Reality (AR) guidance. This work investigates whether visualizing such uncertainty can improve task performance, trust, and confidence. We present four visualization techniques: Cone, Circle, Gauge, and Color. A two-part study evaluated these techniques on a surgical drilling task, first with 24 non-professional participants and then with 4 professional surgeons. Results indicate that uncertainty visualization improved drilling accuracy by 24% but increased task time by 76%. It also enhanced user confidence and trust in the system, with Cone and Circle as the most preferred visualizations. Based on our findings, we discuss design recommendations for integrating uncertainty visualization into AR-based surgical systems. This work paves the way for a higher success rate in surgical procedures.
Visualizing Tree-of-Analysis: Facilitating Conversational Visual Analytics for Novices
Authors: Feiyuan Qu, Tan Tang, Zeyang Fu, Yan Chen 0060, Hanze Jia, Junming Gao, Songela Nurdawulieti, Yingcai Wu
Published at CHI, 2026

Conversational visual analytics (CVA) make data exploration accessible to novices but often leave users disoriented during multi-turn conversations. Previous approaches provide data-centric recommendations, but fail to help users regain orientations. To bridge this gap, we conducted a formative study (N = 12) revealing that novices are insensitive to analytical cues and rely on vague queries, leading to disorientation and task failures. In contrast, experts are sensitive to two types of analytical cues and use seven types of queries to organize workflows. Based on these findings, we propose ToA, a novel approach that structures the CVA process as an interactive analysis tree. Moreover, we visualize this tree, with AI outputs as nodes (containing two cue types) and user queries as edges (categorized by seven query types), to provide novices with an overview of their analysis journey. We evaluated ToA through user studies (N = 12) and expert interviews (N = 3). The results suggest that ToA eliminates task failure and increases per-turn insights (+58.3%), despite longer per-turn thinking time (+17.7%). Expert interviews further confirm its potential to democratize visual analytics.
VueBuds: Visual Intelligence with Wireless Earbuds
Authors: Maruchi Kim, Rasya Fawwaz, Zhi Yang Lim, Brinda Moudgalya, Hexi Wang, Yuanhao Zeng, Shyamnath Gollakota
Published at CHI, 2026

Despite their ubiquity, wireless earbuds remain audio-centric due to size and power constraints. We present VueBuds, the first camera-integrated wireless earbuds for egocentric vision, capable of operating within stringent power and form-factor limits. Each VueBud embeds a camera into a Sony WF-1000XM3 to stream visual data over Bluetooth to a host device for on-device vision language model (VLM) processing. We show analytically and empirically that while each camera's field of view is partially occluded by the face, the combined binocular perspective provides comprehensive forward coverage. By integrating VueBuds with VLMs, we build an end-to-end system for real-time scene understanding, translation, visual reasoning, and text reading; all from low-resolution monochrome cameras drawing under 5mW through on-demand activation. Through online and in-person user studies with 90 participants, we compare VueBuds against smart glasses across 17 visual question-answering tasks, and show that our system achieves response quality on par with Ray-Ban Meta. Our work establishes low-power camera-equipped earbuds as a compelling platform for visual intelligence, bringing rapidly advancing VLM capabilities to one of the most ubiquitous wearable form factors.
"It looks sexy but it's wrong." Tensions in creativity and accuracy using genAI for biomedical visualization
Authors: Roxanne Ziman, Shehryar Saharan, Gaël McGill, Laura A. Garrison
Published at Vis, 2025

We contribute an in-depth analysis of the workflows and tensions arising from generative AI (genAI) use in biomedical visualization (BioMedVis). Although genAI affords facile production of aesthetic visuals for biological and medical content, the architecture of these tools fundamentally limits the accuracy and trustworthiness of the depicted information, from imaginary (or fanciful) molecules to alien anatomy. Through 17 interviews with a diverse group of practitioners and researchers, we qualitatively analyze the concerns and values driving genAI (dis)use for the visual representation of spatially-oriented biomedical data. We find that BioMedVis experts, both in roles as developers and designers, use genAI tools at different stages of their daily workflows and hold attitudes ranging from enthusiastic adopters to skeptical avoiders of genAI. In contrasting the current use and perspectives on genAI observed in our study with predictions towards genAI in the visualization pipeline from prior work, we refocus the discussion of genAI's effects on projects in visualization in the here and now with its respective opportunities and pitfalls for future visualization research. At a time when public trust in science is in jeopardy, we are reminded to first do no harm, not just in biomedical visualization but in science communication more broadly. Our observations reaffirm the necessity of human intervention for empathetic design and assessment of accurate scientific visuals. Supplemental study materials are available at https://osf.io/genaixbiomedvis/.
"Mapping What I Feel": Understanding Affective Geovisualization Design Through the Lens of People-Place Relationships
Authors: Xingyu Lan, Yutong Yang, Yifan Wang
Published at Vis, 2025

Affective visualization design is an emerging research direction focused on communicating and influencing emotion through visualization. However, as revealed by previous research, this area is highly interdisciplinary and involves theories and practices from diverse fields and disciplines, thus awaiting analysis from more fine-grained angles. To address this need, this work focuses on a pioneering and relatively mature sub-area, affective geovisualization design, to further the research in this direction and provide more domain-specific insights. Through an analysis of a curated corpus of affective geovisualization designs using the Person-Process-Place (PPP) model from geographic theory, we derived a design taxonomy that characterizes a variety of methods for eliciting and enhancing emotions through geographic visualization. We also identified four underlying high-level design paradigms of affective geovisualization design (e.g., computational, anthropomorphic) that guide distinct approaches to linking geographic information with human experience. By extending existing affective visualization design frameworks with geographic specificity, we provide additional design examples, domain-specific analyses, and insights to guide future research and practices in this underexplored yet highly innovative domain.
"They Aren't Built For Me": An Exploratory Study of Strategies for Measurement of Graphical Primitives in Tactile Graphics
Authors: Areen Khalaila, Lane Harrison, Nam Wook Kim, Dylan Cashman
Published at Vis, 2025

Advancements in accessibility technologies such as low-cost swell form printers or refreshable tactile displays promise to allow blind or low-vision (BLV) people to analyze data by transforming visual representations directly to tactile representations. However, it is possible that design guidelines derived from experiments on the visual perception system may not be suited for the tactile perception system. We investigate the potential mismatch between familiar visual encodings and tactile perception in an exploratory study into the strategies employed by BLV people to measure common graphical primitives converted to tactile representations. First, we replicate the Cleveland and McGill study on graphical perception using swell form printing with eleven BLV subjects. Then, we present results from a group interview in which we describe the strategies used by our subjects to read four common chart types. While our results suggest that familiar encodings based on visual perception studies can be useful in tactile graphics, our subjects also expressed a desire to use encodings designed explicitly for BLV people. Based on this study, we identify gaps between the perceptual expectations of common charts and the perceptual tools available in tactile perception. Then, we present a set of guidelines for the design of tactile graphics that accounts for these gaps. Supplemental material is available at https://osf.io/3nsfp/?view_only=7b7b8dcbae1d4c9a8bb4325053d13d9f.
A Design Space for Multiscale Visualization
Authors: Mara Solen, Matt I. B. Oddo, Tamara Munzner
Published at Vis, 2025

Designing multiscale visualizations, particularly when the ratio between the largest scale and the smallest item is large, can be challenging, and designers have developed many approaches to overcome this challenge. We present a design space for visualization with multiple scales. The design space includes three dimensions, with eight total subdimensions. We demonstrate its descriptive power by using it to code approaches from a corpus we compiled of 52 examples, created by a mix of academics and practitioners. We demonstrate descriptive power by analyzing and partitioning these examples into four high-level strategies for designing multiscale visualizations, which are shared approaches with respect to design space dimension choices. We demonstrate generative power by analyzing missed opportunities within the corpus of examples, identified through analysis of the design space, where we note how certain examples could have benefited from different choices. We discuss patterns in the use of different dimension and strategy choices in the different visualization contexts of analysis and presentation. Supplemental materials: https://osf.io/wbrdm/ Design space website: https://marasolen.github.io/multiscale-vis-ds/
A Multimodal Framework for Understanding Collaborative Design Processes
Authors: Maurice Koch, Nelusa Pathmanathan, Daniel Weiskopf, Kuno Kurzhals
Published at Vis, 2025

An essential task in analyzing collaborative design processes, such as those that are part of workshops in design studies, is identifying design outcomes and understanding how the collaboration between participants formed the results and led to decision-making. However, findings are typically restricted to a consolidated textual form based on notes from interviews or observations. A challenge arises from integrating different sources of observations, leading to large amounts and heterogeneity of collected data. To address this challenge we propose a practical, modular, and adaptable framework of workshop setup, multimodal data acquisition, AI-based artifact extraction, and visual analysis. Our interactive visual analysis system, reCAPit, allows the flexible combination of different modalities, including video, audio, notes, or gaze, to analyze and communicate important workshop findings. A multimodal streamgraph displays activity and attention in the working area, temporally aligned topic cards summarize participants' discussions, and drill-down techniques allow inspecting raw data of included sources. As part of our research, we conducted six workshops across different themes ranging from social science research on urban planning to a design study on band-practice visualization. The latter two are examined in detail and described as case studies. Further, we present considerations for planning workshops and challenges that we derive from our own experience and the interviews we conducted with workshop experts. Our research extends existing methodology of collaborative design workshops by promoting data-rich acquisition of multimodal observations, combined AI-based extraction and interactive visual analysis, and transparent dissemination of results.
A Rigorous Behavior Assessment of CNNs Using a Data-Domain Sampling Regime
Authors: Shuning Jiang, Wei-Lun Chao, Daniel Haehn, Hanspeter Pfister, Jian Chen 0006
Published at Vis, 2025

We present a data-domain sampling regime for quantifying CNNs' graphic perception behaviors. This regime lets us evaluate CNNs' ratio estimation ability in bar charts from three perspectives: sensitivity to training-test distribution discrepancies, stability to limited samples, and relative expertise to human observers. After analyzing 16 million trials from 800 CNN models and 6,825 trials from 113 human participants, we arrived at a simple and actionable conclusion: CNNs can outperform humans and their biases simply depend on the training-test distance. We show evidence of this simple, elegant behavior of the machines when they interpret visualization images. osf.io/gfqc3 provides registration, the code for our sampling regime, and experimental results.
Affective color scales for colormap data visualizations
Authors: Halle C. Braun, Kushin Mukherjee, Seth R. Gorelik, Karen B. Schloss
Published at Vis, 2025

Research on affective visualization design has shown that color is an especially powerful feature for influencing the emotional connotation of visualizations. Associations between colors and emotions are largely driven by lightness (e.g., lighter colors are associated with positive emotions, whereas darker colors are associated with negative emotions). Designing visualizations to have all light or all dark colors to convey particular emotions may work well for visualizations in which colors represent categories and spatial channels encode data values. However, this approach poses a problem for visualizations that use color to represent spatial patterns in data (e.g., colormap data visualizations) because lightness contrast is needed to reveal fine details in spatial structure. In this study, we found it is possible to design colormaps that have strong lightness contrast to support spatial vision while communicating clear affective connotation. We also found that affective connotation depended not only on the color scales used to construct the colormaps, but also the frequency with which colors appeared in the map, as determined by the underlying dataset (data-dependence hypothesis). These results emphasize the importance of data-aware design, which accounts for not only the design features that encode data (e.g., colors, shapes, textures), but also how those design features are instantiated in a visualization, given the properties of the data.
Algorithmically-Assisted Schematic Transit Map Design: A System and Algorithmic Core for Fast Layout Iteration
Authors: Thomas C. van Dijk, Soeren Terziadis
Published at Vis, 2025

London's famous “tube map” is an iconic piece of design and perhaps represents the schematic visualization style most well-known to the general public: its octolinearity has become the de facto standard for transit maps around the world. Making a good schematic transit map is challenging and labour-intensive, and has attracted the attention of the optimization community. Much of the literature has focused on mathematically defining an optimal drawing and algorithms to compute one. However, achieving these “optimal” layouts is computationally challenging, often requiring multiple minutes of runtime. Crucially, what it means for a map to be good is actually highly dependent on factors that evade a general formal definition, like unique landmarks within the network, the context in which a map will be displayed, and the preference of the designer and client. Rather than attempting to make an algorithm that produces a single high-quality and ready-to-use metro map at great cost, we propose it is more fruitful to support rapid layout iteration by a human designer, providing a workflow that enables efficient exploration of a wider range of designs than could be done by hand, and iterating on these designs. To this end we identify steps in the design process of schematic maps that are tedious to do by hand but are algorithmically feasible, and present a framework around a simple linear program that computes network layouts almost instantaneously given a fixed direction for every connection. These connection directions are decided by a designer in a graphical user interface with several interaction methods and a number of quality-of-life features demonstrating the flexibility of the framework; the implementation is available as open source.
An Analysis of Text Functions in Information Visualization
Authors: Chase Stokes, Anjana Arunkumar, Marti A. Hearst, Lace M. K. Padilla
Published at Vis, 2025

Text is an integral but understudied component of visualization design. Although recent studies have examined how text elements (e.g., titles and annotations) influence comprehension, preferences, and predictions, many questions remain about textual design and use in practice. This paper introduces a framework for understanding text functions in information visualizations, building on and filling gaps in prior classifications and taxonomies. Through an analysis of 120 real-world visualizations and 804 text elements, we identified ten distinct text functions, ranging from identifying data mappings to presenting valenced subtext. We further identify patterns in text usage and conduct a factor analysis, revealing four overarching text-informed design strategies: Attribution and Variables, Annotation-Centric Design, Visual Embellishments, and Narrative Framing. In addition to these factors, we explore features of title rhetoric and text multifunctionality, while also uncovering previously unexamined text functions, such as text replacing visual elements. Our findings highlight the flexibility of text, demonstrating how different text elements in a given design can combine to communicate, synthesize, and frame visual information. This framework adds important nuance and detail to existing frameworks that analyze the diverse roles of text in visualization.
An Autoethnography on Visualization Literacy: A Wicked Measurement Problem
Authors: Lily W. Ge, Anne-Flore Cabouat, Karen Bonilla, Yuan Cui, Yiren Ding, Noëlle Rakotondravony, Mackenzie Michael Creamer, Jasmine Tan Otto, Maryam Hedayati, Bum Chul Kwon, Angela Locoro, Lane Harrison, Petra Isenberg, Michael Correll, Matthew Kay 0001
Published at Vis, 2025

We contribute an autoethnographic reflection on the complexity of defining and measuring visualization literacy (i.e., the ability to interpret and construct visualizations) to expose our tacit thoughts that often exist in-between polished works and remain unreported in individual research papers. Our work is inspired by the growing number of empirical studies in visualization research that rely on visualization literacy as a basis for developing effective data representations or educational interventions. Researchers have already made various efforts to assess this construct, yet it is often hard to pinpoint either what we want to measure or what we are effectively measuring. In this autoethnography, we gather insights from 14 internal interviews with researchers who are users or designers of visualization literacy tests. We aim to identify what makes visualization literacy assessment a “wicked” problem. We further reflect on the fluidity of visualization literacy and discuss how this property may lead to misalignment between what the construct is and how measurements of it are used or designed. We also examine potential threats to measurement validity from conceptual, operational, and methodological perspectives. Based on our experiences and reflections, we propose several calls to action aimed at tackling the wicked problem of visualization literacy measurement, such as by broadening test scopes and modalities, improving test ecological validity, making it easier to use tests, seeking interdisciplinary collaboration, and drawing from continued dialogue on visualization literacy to expect and be more comfortable with its fluidity.
An Intelligent Interactive Visual Analytics System for Exploring Large and Multi-Scale Pathology Images
Authors: Chaoqing Xu, Ruiqi Yang, Weihan Li, Xinyuan Fu, Liting Fang, Zunlei Feng, Xiuming Zhang, Can Wang 0001, Mingli Song, Wei Chen 0001
Published at Vis, 2025

Pathology images are crucial for cancer diagnosis and treatment. Although artificial intelligence has driven rapid advancements in pathology image analysis, the interpretation of ultra-large and multi-scale pathology images in clinical practice still heavily relies on physicians' experience. Clinicians need to repeatedly zoom in and out on individual slides to compare and assess pathological details - a process that is both time-consuming and prone to visual fatigue. The system first employs a diffusion model to perform tissue segmentation on pathology images, then calculates pathological tissue proportions and morphological metrics. Finally, through multi-scale dynamic comparison and multi-level visual evaluation, the system facilitates comprehensive and precise analysis of pathology images. The system provides clinicians with an intelligent and interactive tool for pathology image interpretation, enabling efficient visualization and precise analysis of pathological details, thereby reducing the effort require for detailed analysis.