DataSway: Vivifying Metaphoric Visualization with Animation Clip Generation and Coordination
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914507077451776 |
|---|---|
| author | Xie, Liwenhan Zhou, Jiayi Rao, Anyi Qu, Huamin Shu, Xinhuan |
| author_facet | Xie, Liwenhan Zhou, Jiayi Rao, Anyi Qu, Huamin Shu, Xinhuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22051 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DataSway: Vivifying Metaphoric Visualization with Animation Clip Generation and Coordination Xie, Liwenhan Zhou, Jiayi Rao, Anyi Qu, Huamin Shu, Xinhuan Human-Computer Interaction 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. |
| title | DataSway: Vivifying Metaphoric Visualization with Animation Clip Generation and Coordination |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.22051 |