SceneLoom: Communicating Data with Scene Context
Lin Gao, Leixian Shen, Yuheng Zhao, Jiexiang Lan, Huamin Qu, Siming Chen

TL;DR
SceneLoom is a system that uses vision-language models to help creators align data visualizations with real-world images, enhancing storytelling by improving visual coherence and narrative expressiveness.
Contribution
The paper introduces SceneLoom, a novel system that automates the coordination of data visualizations with scene images using VLMs, addressing a gap in narrative data storytelling tools.
Findings
SceneLoom effectively generates coherent visual and semantic alignments.
Users find SceneLoom useful for creative design and externalizing ideas.
The system supports interactive refinement and animated transitions.
Abstract
In data-driven storytelling contexts such as data journalism and data videos, data visualizations are often presented alongside real-world imagery to support narrative context. However, these visualizations and contextual images typically remain separated, limiting their combined narrative expressiveness and engagement. Achieving this is challenging due to the need for fine-grained alignment and creative ideation. To address this, we present SceneLoom, a Vision-Language Model (VLM)-powered system that facilitates the coordination of data visualization with real-world imagery based on narrative intents. Through a formative study, we investigated the design space of coordination relationships between data visualization and real-world scenes from the perspectives of visual alignment and semantic coherence. Guided by the derived design considerations, SceneLoom leverages VLMs to extract…
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Taxonomy
TopicsData Visualization and Analytics · Interactive and Immersive Displays · Computer Graphics and Visualization Techniques
