DevLan/vispubs huggingface dataset:
Automatic Synthesis of Visualization Design Knowledge Bases
Authors: Hyeok Kim, Sehi L'Yi, Nils Gehlenborg, Jeffrey Heer
Published at CHI, 2026

Formal representations of the visualization design space, such as knowledge bases and graphs, consolidate design practices into a shared resource and enable automated reasoning and interpretable design recommendations. However, prior approaches typically depend on fixed, manually authored rules, making it difficult to build novel representations or extend them for different visualization domains. Instead, we propose data-driven methods that automatically synthesize visualization design knowledge bases. Specifically, our methods (1) extract candidate design features from a visualization corpus, (2) select features forward and backward, and (3) render the final knowledge base. In our benchmark evaluation compared to Draco 2, our synthesized knowledge base offers general and interpretable design features and improves the accuracy of predicting effective designs by 1–15% in varied training and test sets. When we apply our approach to genomics visualization, the synthesized knowledge base includes sensible features with accuracy up to 97%, demonstrating the applicability of our approach to other visualization domains.
BAIT: Visual-illusion-inspired Privacy Preservation for Mobile Data Visualization
Authors: Sizhe Cheng, Songheng Zhang, Dong Ma 0001, Yong Wang 0021
Published at CHI, 2026

With the prevalence of mobile data visualizations, there have been growing concerns about their privacy risks, especially shoulder surfing attacks. Inspired by prior research on visual illusion, we propose BAIT, a novel approach to automatically generate privacy-preserving visualizations by stacking a decoy visualization over a given visualization. It allows visualization owners at proximity to clearly discern the original visualization and makes shoulder surfers at a distance be misled by the decoy visualization, by adjusting different visual channels of a decoy visualization (e.g., shape, position, tilt, size, color and spatial frequency). We explicitly model human perception effect at different viewing distances to optimize the decoy visualization design. Privacy-preserving examples and two in-depth user studies demonstrate the effectiveness of BAIT in both controlled lab study and real-world scenarios.
BloomBeacon: Blooming Physical Touch Display Surfaces via Persistence-of-Vision Motion
Authors: Willa Yunqi Yang, Justice T. Andersen, David Dajun Yuan, Ken Nakagaki
Published at CHI, 2026

We explore how a display surface can physically emerge on demand to support both mid-air visualization and direct touch interaction. We introduce blooming, a concept that repurposes Persistence-of-Vision (POV) motion to deploy a large, touchable surface from a compact, relocatable device. Using a soft, rotating line with arch-shaped electrodes, our system renders dynamic mid-air visuals while enabling direct touch input on the manifested surface. Realizing this concept requires addressing the unique challenges of touching spinning elements, including ensuring safety, minimizing disturbances to rotation caused by touch, and detecting brief unstable touches during spinning. We present a safety-oriented device design, special blades effective in minimizing finger disturbance, and optimization techniques tailored to transient, noisy touches. We also reveal how rotation speed and electrode height significantly affect sensing accuracy and user experience. Finally, we demonstrate applications that show how blooming touch displays can flexibly augment everyday objects and environments.
BRIDGE: Borderless Reconfiguration for Inclusive and Diverse Gameplay Experience via Embodiment Transformation
Authors: Hayato Saiki, Chunggi Lee, Hikari Takahashi, Tica Lin, Hidetada Kishi, Kaori Tachibana, Yasuhiro Suzuki, Hanspeter Pfister, Kenji Suzuki 0002
Published at CHI, 2026

Training resources for parasports are limited, reducing opportunities for athletes and coaches to engage with sport-specific movements and tactical coordination. To address this gap, we developed BRIDGE, a system that integrates a reconstruction pipeline, which detects and tracks players from broadcast video to generate 3D play sequences, with an embodiment-aware visualization framework that decomposes head, trunk, and wheelchair base orientations to represent attention, intent, and mobility. We evaluated BRIDGE in two controlled studies with 20 participants (10 national wheelchair basketball team players and 10 amateur players). The results showed that BRIDGE significantly enhanced the perceived naturalness of player postures and made tactical intentions easier to understand. In addition, it supported functional classification by realistically conveying players' capabilities, which in turn improved participants' sense of self-efficacy. This work advances inclusive sports learning and accessible coaching practices, contributing to more equitable access to tactical resources in parasports.
CADModelScope: Revealing the Dependency Structure Behind Parametric Computer-Aided Design Models
Authors: Yuanzhe Deng, Zhijing Zhang, Shurui Zhou, Alison Olechowski
Published at CHI, 2026

Parametric computer-aided design (CAD) models are constructed by a sequence of operations, where each operation may reference geometries created by earlier operations. This network of dependencies enables efficient modelling of complex geometry but also results in fragile models, where small modifications can trigger cascading errors. These interdependencies are obscured in commercial CAD systems, leaving users to rely on trial and error when navigating, modularizing, and debugging unfamiliar and complex models. In this paper, we motivate, present, and pilot CADModelScope, a multi-level graph-based visualization of operation dependencies integrated into a commercial CAD platform. In a qualitative lab study, we observed how participants locate and interpret operations, and how CADModelScope enhances awareness of hidden interdependencies and supports more structured navigation. Our findings highlight the potential of using the network of operation dependency as an effective representation for understanding and interacting with parametric CAD models, and we discuss implications for future tool design.
Can AR Embedded Visualizations Foster Appropriate Reliance on AI in Spatial Decision-Making? A Comparative Study of AR X-Ray vs. 2D Minimap
Authors: Xianhao Carton Liu, Difan Jia, Tongyu Nie, Evan Suma Rosenberg, Victoria Interrante, Chen Zhu-Tian
Published at CHI, 2026

Artificial Intelligence (AI) and indoor sensing increasingly support decision-making in spatial environments. However, traditional visualization methods impose a substantial mental workload when viewers translate this digital information into real-world spaces, leading to inappropriate reliance on AI. Embedded visualizations in Augmented Reality (AR), by integrating information into physical environments, may reduce this workload and foster more appropriate reliance on AI. To assess this, we conducted an empirical study (N = 32) comparing an AR embedded visualization (X-ray) and 2D Minimap in AI-assisted, time-critical spatial target selection tasks. Surprisingly, evidence shows that the embedded visualization led to greater inappropriate reliance on AI, primarily as over-reliance, due to factors like perceptual challenges, visual proximity illusions, and highly realistic visual representations. Nonetheless, the embedded visualization showed benefits in spatial mapping. We conclude by discussing empirical insights, design implications, and directions for future research on human-AI collaborative decision in AR.
Challenges in Synchronous & Remote Collaboration Around Visualization
Authors: Matthew Brehmer, Maxime Cordeil, Christophe Hurter, Takayuki Itoh, Wolfgang Büschel, Mahmood Jasim, Arnaud Prouzeau, David Saffo, Lyn Bartram, Sheelagh Carpendale, Chen Zhu-Tian, Andrew Cunningham, Tim Dwyer, Samuel Huron, Masahiko Itoh, Alark Joshi, Kiyoshi Kiyokawa, Hideaki Kuzuoka, Bongshin Lee, Gabriela Molina León, Harald Reiterer, Bektur Ryskeldiev, Jonathan A. Schwabish, Brian A. Smith 0001, Yasuyuki Sumi, Ryo Suzuki 0001, Anthony Tang 0001, Yalong Yang 0001, Jian Zhao 0010
Published at CHI, 2026

We characterize 16 challenges faced by those investigating and developing remote and synchronous collaborative experiences around visualization. Our work reflects the perspectives and prior research efforts of an international group of 29 experts from across human-computer interaction and visualization sub-communities. The challenges are anchored around five collaborative activities that exhibit a centrality of visualization and multimodal communication. These activities include exploratory data analysis, creative ideation, visualization-rich presentations, joint decision making grounded in data, and real-time data monitoring. The challenges also reflect the changing dynamics of these activities in the face of recent advances in extended reality (XR) and artificial intelligence (AI). As an organizing scheme for future research at the intersection of visualization and computer-supported cooperative work, we align the challenges with a sequence of four sets of research and development activities: technological choices, social factors, AI assistance, and evaluation.
Chasing Meaning and/or Insight? A Survey on Evaluation Practices at the Intersection of Visualization and the Humanities
Authors: Alejandro Benito-Santos, Florian Windhager, Aida Horaniet Ibañez, Rabea Kleymann, Alfie Abdul-Rahman, Eva Mayr
Published at CHI, 2026

The intersection of visualization and the humanities (VIS*H) is marked by a tension between chasing analytical "insight" and interpretive "meaning." The effectiveness of visualization techniques hinges on established evaluation frameworks that assess both analytical utility and communicative efficacy, creating a potential mismatch with the non-positivist, interpretive aims of humanities scholarship. To examine how this tension manifests in practice, we systematically surveyed 171 VIS*H design studies to analyze their evaluation workflows and rigor according to standard practice. Our findings reveal recurring flaws, such as an over-reliance on monomethod approaches, and show that higher-quality evaluations emerge from workflows that effectively triangulate diverse evidence. From these findings, we derive recommendations to refine quality and validation criteria for humanities visualizations, and juxtapose them to ongoing critical debates in the field, ultimately arguing for a paradigm shift that can reconcile the advantages of established validation techniques with the interpretive depth required for humanistic inquiry.
CineCraft: Unified Shot Planning, Capture, and Post-Processing for Mobile Cinematography
Authors: Nhan (Nathan) Tran, Sam Belliveau, Zixin Xu, Abe Davis
Published at CHI, 2026

We present CineCraft, an interactive mobile application that unifies planning, capture, and post-processing for cinematography on a single device. Our key design insight is to use a storyboard-like shot plan as a persistent representation that connects different stages of the filmmaking process, emulating coordination strategies used by professional film crews. Our shot plans extend common storyboarding conventions to encode time-varying parameters (e.g., camera movement, focus, and zoom) on a shared timeline, enabling previsualization during planning and precise synchronization during capture. CineCraft uses shot plans to generate camera movement instructions, provide augmented-reality (AR) framing guidance during filming, automate focus and zoom, and organize takes for review and rough-cut assembly. By consolidating stages that are often fragmented across separate mobile apps and ad hoc workflows, our system enables rapid on-location iteration with immediate playback. We demonstrate our system through a range of examples and two user studies.
ClayPhys: Towards Toolkits that Support Making Expressive Data Physicalization
Authors: Bahare Bakhtiari, Yichun Zhao, Jiayue Wong, Aurélien Tabard, Sowmya Somanath, Charles Perin
Published at CHI, 2026

Existing toolkits for data physicalization prioritize ease of use and adoption by novices. This is often achieved by limiting the affordances of the materials used and constraining design possibilities. The result is limited opportunities for creating expressive physicalizations. To address this limitation and to better understand how to support the creation process of expressive physicalizations, we created ClayPhys, a low-fidelity data physicalization toolkit designed to encourage making expressive data physicalizations. Our toolkit consists of clay, clay work tools, instruction and documentation handbooks, and warm-up activities that scaffold the design process. We studied the use of ClayPhys in a one-day workshop with nine expert participants. From our analysis of participants' created data physicalizations, we observed that using ClayPhys, participants could map data to different visual and physical variables, and that their designs incorporated various data interaction styles. Informed by our findings, we discuss implications for designing higher-fidelity expressive data physicalization toolkits.
CodeStream: Augmenting Timelines with Code Annotation for Navigating Large Coding Histories
Authors: Ashley Ge Zhang, Yan-Ru Jhou, Yinuo Yang, Shamita Rao, Maryam Arab, Yan Chen 0033, Steve Oney
Published at CHI, 2026

Code edit histories can offer instructors valuable insight into students' problem-solving processes, revealing unproductive behaviors that final code alone cannot capture. For example, a correct solution may contain large copy-and-pasted segments (suggesting the code originated elsewhere) or unguided trial-and-error (suggesting a lack of clear strategy). Timelines are a common way to visualize code histories, but existing timeline visualizations of code or document histories show only when and where edits occurred, not what changed. Without this context, it is difficult to answer key questions about how students invested effort or to infer their intentions. We present CodeStream, a visualization system that augments timelines with situational code annotations, whose granularity and visibility dynamically adapt to scale and interaction state. A comparison study shows that CodeStream enables context-aware navigation of coding histories, supporting fast and accurate pattern identification, and helping instructors reason about students' coding behaviors and identify who may need intervention.
Component-Wise Sketching and Generation for Car Interior Design
Authors: Jeongche Yoon, Seung-Jun Lee, Donghyeok Ma, Seok-Hyung Bae
Published at CHI, 2026

While car exterior designers are adopting 2D generative AI in their workflows, interior designers still face significant challenges. AI often fails to capture intended forms and layouts from interior sketches typically drawn in extreme perspectives and containing multiple components. Moreover, designers must rely on extensive manual work to explore numerous combinations of various components across different views. Thus, we propose a novel workflow in which designers sketch components individually, generate refined renderings for each component, and compose them to pre-visualize concepts. This pre-visualization then serves as input for generating high-quality renderings. We first interviewed interior designers to identify key challenges. We next developed a proof-of-concept system and conducted a pilot study to test its usefulness. We finally improved the system based on additional requirements and conducted a formal user study, demonstrating that our simple yet effective workflow supports designers in exploring, composing, and refining cabin designs into high-quality results.
CoNode: Visualizing Workflows for Knowledge Reuse and Recombination in Team-AI Collaborative Design
Authors: Yang Zhou, Yi Xiao 0004, Guangpeng Wei, Suxiang Ling, Ruoxuan Ma, Chi-Sing Leung
Published at CHI, 2026

In early-stage industrial design, teams generate essential but fragile process knowledge—semantic tags, sketches, exploration paths—that is rarely captured or reused but which may be useful at latter design stages, and AI could be used for this purpose. Yet existing AI creativity tools remain outcome-oriented, offering limited support for preserving, tracing, or recombining underlying reasoning. Our formative study (N=6) revealed persistent challenges in team–AI ideation across sessions and collaborators, including semantic–visual fragmentation, context loss, and cross-tool disruption. These insights inspired CoNode, a two-layer system that embeds AI nodes within a shared whiteboard through triplet workflows and augments them with workflow-level consolidation, reuse, and recombination via the CoSense module. We conducted a two-stage evaluation: User Study I (N=12) validates CoNode's foundational interaction paradigm layer, and User Study II (N=30) evaluates its process-oriented knowledge layer. Results show that CoNode significantly improves knowledge consolidation, reuse, and recombination, effectively facilitating the collaborative processes and demonstrating how generative AI can evolve process knowledge across collaborative rounds.
Consensus and Contradictions: A Cross-Organizational Analysis of Visualization Style Guides
Authors: Alvitta Ottley
Published at CHI, 2026

Should you place three pie charts side by side, or should you avoid pie charts altogether? Publicly available visualization style guides offer contradictory answers to such questions. Despite their growing influence on how people encounter data, these guides are seldom studied as a collective phenomenon. Addressing this gap, this paper presents the first systematic analysis of 53 publicly accessible visualization style guides from diverse domains, including journalism, government, non-profit, corporate, and academic sectors. We build a standardized corpus, conduct a multi-method analysis that reveals both consensus and contradiction, and develop a companion Guidelines Explorer to support transparency and future use. This work sheds light on organizational visualization design norms and provides a foundation for future work that helps bridge the gap between academic and industry practices. In doing so, we help reframe style guides as sociotechnical artifacts that encode values as much as design rules.
ContAQT: Designing an Interactive Data Display to Make Multi-Pollutant Air Quality Data Accessible
Authors: Yixuan Li, Jordan Hill, Zeyu Hua, Seik Oh, Yuhan Wang, Alex Endert, Jessica Roberts
Published at CHI, 2026

Air pollution poses critical health and environmental risks, yet public communication of air quality data is typically reduced to a single Air Quality Index (AQI) value that obscures underlying complexity. Existing visualization tools reinforce this oversimplification, leaving key AQI messages unaddressed. This paper presents ContAQT, an interactive platform that helps public audiences explore multi-pollutant air quality data in informal learning settings. Using a learner-centered design (LCD) approach, we conducted a three-step study: (1) surveys with novices and interviews with experts to identify pitfalls in current AQI communication tools, (2) iterative design and evaluation across multiple design cycles, and (3) evaluation at a local science festival. Findings show how interactive visualizations can address persistent misconceptions, spark inquiry, and support sense-making around air quality data. We contribute ContAQT as a public data display and derive design implications for building interactive systems that make complex scientific data accessible in informal learning spaces.
Creativity from Surprise: Bridging the Gap Between Fashion Designers' Inspiration Work and AI Creative Support Tools
Authors: Yu Jin, Yousang Kwon, Juhyeok Yoon, Bowen Zhan, Kyungho Lee
Published at CHI, 2026

Advances in Generative AI (GenAI) enable unexpected creation in visual images. In fashion design, this capability has intensified demand for creativity support tools where fast-paced trends challenge fixation and drive exploration of novel creative directions. While prior work has explored interfaces that align designer intent with GenAI outputs, we still lack an empirical understanding of how fashion designers define, seek, and utilize AI-generated surprise as a valuable resource and actionable design direction rather than random noise. We address this gap through a qualitative study combining semi-structured interviews with 20 fashion professionals and a design workshop with 12 graduate students. We conceptualized surprise as a strategy that can be designed into GenAI-powered visualization tools to support traceable exploration, contextual grounding, and controllable variation across ideation stages. This work (1) reframes surprise as a designable mechanism or resource for co-creative interaction, (2) provides empirical insights into how fashion designers can utilize AI-generated surprise in the early stage of design, and (3) translates these insights into actionable guidance for building GenAI-driven visualization tools for fashion and related creative domains from a human-centered AI perspective.
Crystallizing Schemas with Teleoscope: Thematic Curation of Large Text Corpora on Reddit
Authors: Patrick Yung Kang Lee, Paul H. Bucci, Leo Itsuki Foord-Kelcey, Alamjeet Singh, Ivan Beschastnikh
Published at CHI, 2026

Large text corpora, such as Reddit posts, have become an increasingly prevalent site of qualitative inquiry. However, most large text corpora are intractable for qualitative researchers. Instead, teams rely on statistical subsampling to reduce corpora to a manageable size for qualitative analysis. While previous work for navigating large corpora involves visualizing the dataset at the corpus-level using high-level statistical summaries, few systems offer the ability to curate data using an interpretivist approach. To address this, we developed Teleoscope, a web-based interface designed to scaffold iterative, interactive, and reflexive refinement of a large corpus, in a process we call thematic curation. Across three deployments, we learned that Teleoscope supports serendipitous discovery of new keywords, results in greater feelings of confidence in search saturation, and aids collaborative discussion of alternative curation pathways. Teleoscope empowers researchers to stay "close to the data" in order to make qualitative workflows methodologically coherent with large text corpora.
D-MO: Depth from Motion and Occlusion as a Visual Channel for Information Visualization
Authors: Carla Coutant, Adrien Chaffangeon Caillet, Renaud Blanch
Published at CHI, 2026

On a visualization, the position of the marks encoding data is the most expressive and effective visual channel. It conveys order and quantity without impairing the perception of other visual channels. In the field of Information Visualization, position is often restricted to two dimensions, because using the third dimension, depth, usually affects the perception of size, which is also one of the most effective visual channels. We propose a new visual channel, D-MO (Depth from Motion and Occlusion), a combination of visual cues, motion and occlusion, with interactions, that induces a depth perception suitable for combined use with classic visual channels. We characterize the expressiveness and effectiveness of D-MO and show that it is a magnitude channel with good accuracy, acceptable discriminability, and is separable from size. Thus, D-MO opens up new areas for visualization design, which is limited by the scarceness of available visual channels.