DataSway: Vivifying Metaphoric Visualization with Animation Clip Generation and Coordination

2025-07-29

AI Summary

Metaphorical data visualization animations face three core challenges: semantic consistency, data fidelity, and seamless interaction integration. To address these, we propose a human-in-the-loop SVG animation generation framework that leverages vision-language models (VLMs) to interpret metaphorical semantics and generate semantically aligned animation segments. A configurable timeline orchestration mechanism dynamically coordinates animation timing along dimensions such as data attributes and spatial layout. Our prototype system, DataSway, enables end-to-end generation of interactive SVG animations from metaphorical concepts. A user study demonstrates significant improvements in creators’ expressive efficiency and visualization usability; a curated case library validates practical applicability in web-based hypermedia contexts. This work is the first to deeply integrate VLMs into the closed-loop generation and orchestration of metaphorical visualization animations, advancing data storytelling toward semantics-driven, human–AI co-creative paradigms.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Computer Vision: Visual Reasoning & Symbolic Representations
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Web data visualization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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 6 cases demonstrates its capabilities and applications in web-based hypermedia. We conclude with implications for future research on bespoke data visualization animation.

Problem

• Designing metaphoric visualization animations with semantic motion alignment
• Maintaining accurate data representation during dynamic visualization
• Integrating interactivity seamlessly into animated data visualizations

Innovation

• Human-AI co-creation for SVG animations
• Vision-language models generate animation clips
• Timeline coordination by data attributes