Xinhuan Shu

Liwenhan Xie, Jiayi Zhou, Anyi Rao et al.

Animating metaphoric visualizations brings data to life, enhancing the comprehension of abstract data encodings and fostering deeper engagement. However, creators face significant challenges in designing these animations, such as crafting motions that align semantically with the metaphors, maintaining faithful data representation during animation, and seamlessly integrating interactivity. We propose a human-AI co-creation workflow that facilitates creating animations for SVG-based metaphoric visualizations. Users can initially derive animation clips for data elements from vision-language models (VLMs) and subsequently coordinate their timelines based on entity order, attribute values, spatial layout, or randomness. Our design decisions were informed by a formative study with experienced designers (N=8). We further developed a prototype, DataSway, and conducted a user study (N=14) to evaluate its creativity support and usability. A gallery with seven cases demonstrates its capabilities and applications in web-based hypermedia. We conclude with implications for future research on bespoke data visualization animation.

Zhen Li, Duan Li, Yukai Guo et al. · tsinghua

Infographic charts are a powerful medium for communicating abstract data by combining visual elements (e.g., charts, images) with textual information. However, their visual and structural richness poses challenges for large vision-language models (LVLMs), which are typically trained on plain charts. To bridge this gap, we introduce ChartGalaxy, a million-scale dataset designed to advance the understanding and generation of infographic charts. The dataset is constructed through an inductive process that identifies 75 chart types, 440 chart variations, and 68 layout templates from real infographic charts and uses them to create synthetic ones programmatically. We showcase the utility of this dataset through: 1) improving infographic chart understanding via fine-tuning, 2) benchmarking code generation for infographic charts, and 3) enabling example-based infographic chart generation. By capturing the visual and structural complexity of real design, ChartGalaxy provides a useful resource for enhancing multimodal reasoning and generation in LVLMs.

Aoyu Wu, Yun Wang, Xinhuan Shu et al.

Visualizations themselves have become a data format. Akin to other data formats such as text and images, visualizations are increasingly created, stored, shared, and (re-)used with artificial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data (AI4VIS). We define visualization data as the digital representations of visualizations in computers and focus on data visualization (e.g., charts and infographics). We build our survey upon a corpus spanning ten different fields in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of surveyed papers and related material available online at ai4vis.github.io.

Junxiu Tang, Lingyun Yu, Tan Tang et al.

Transitions are widely used in data videos to seamlessly connect data-driven charts or connect visualizations and non-data-driven motion graphics. To inform the transition designs in data videos, we conduct a content analysis based on more than 3500 clips extracted from 284 data videos. We annotate visualization types and transition designs on these segments, and examine how these transitions help make connections between contexts. We propose a taxonomy of transitions in data videos, where two transition categories are defined in building fluent narratives by using visual variables.

Xinhuan Shu, Aoyu Wu, Junxiu Tang et al.

GIFs are enjoying increasing popularity on social media as a format for data-driven storytelling with visualization; simple visual messages are embedded in short animations that usually last less than 15 seconds and are played in automatic repetition. In this paper, we ask the question, "What makes a data-GIF understandable?" While other storytelling formats such as data videos, infographics, or data comics are relatively well studied, we have little knowledge about the design factors and principles for "data-GIFs". To close this gap, we provide results from semi-structured interviews and an online study with a total of 118 participants investigating the impact of design decisions on the understandability of data-GIFs. The study and our consequent analysis are informed by a systematic review and structured design space of 108 data-GIFs that we found online. Our results show the impact of design dimensions from our design space such as animation encoding, context preservation, or repetition on viewers' understanding of the GIF's core message. The paper concludes with a list of suggestions for creating more effective Data-GIFs.

Dazhen Deng, Yihong Wu, Xinhuan Shu et al.

Images in visualization publications contain rich information, e.g., novel visualization designs and implicit design patterns of visualizations. A systematic collection of these images can contribute to the community in many aspects, such as literature analysis and automated tasks for visualization. In this paper, we build and make public a dataset, VisImages, which collects 12,267 images with captions from 1,397 papers in IEEE InfoVis and VAST. Built upon a comprehensive visualization taxonomy, the dataset includes 35,096 visualizations and their bounding boxes in the images.We demonstrate the usefulness of VisImages through three use cases: 1) investigating the use of visualizations in the publications with VisImages Explorer, 2) training and benchmarking models for visualization classification, and 3) localizing visualizations in the visual analytics systems automatically.

Aoyu Wu, Bon Kyung Ku, Furui Cheng et al.

To enhance the mobility and convenience of the campus community, we designed and implemented the Pulse system, a visual interface for communicating the crowd information to the lay public including campus members and visitors. This is a challenging task which requires analyzing and reconciling the demands and interests for data as well as visual design among diverse target audiences. Through an iterative design progress, we study and address the diverse preferences of the lay audiences, whereby design rationales are distilled. The final prototype combines a set of techniques such as chart junk and redundancy encoding. Initial feedback from a wide audience confirms the benefits and attractiveness of the system.