SmartShots: An Optimization Approach for Generating Videos with Data Visualizations Embedded

Tan Tang, Junxiu Tang, Jiewen Lai, Lu Ying, Yingcai Wu*, Lingyun Yu, Peiran Ren

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Videos are well-received methods for storytellers to communicate various narratives. To further engage viewers, we introduce a novel visual medium where data visualizations are embedded into videos to present data insights. However, creating such data-driven videos requires professional video editing skills, data visualization knowledge, and even design talents. To ease the difficulty, we propose an optimization method and develop SmartShots, which facilitates the automatic integration of in-video visualizations. For its development, we first collaborated with experts from different backgrounds, including information visualization, design, and video production. Our discussions led to a design space that summarizes crucial design considerations along three dimensions: Visualization, embedded layout, and rhythm. Based on that, we formulated an optimization problem that aims to address two challenges: (1) embedding visualizations while considering both contextual relevance and aesthetic principles and (2) generating videos by assembling multi-media materials. We show how SmartShots solves this optimization problem and demonstrate its usage in three cases. Finally, we report the results of semi-structured interviews with experts and amateur users on the usability of SmartShots.

Original languageEnglish
Article number4
JournalACM Transactions on Interactive Intelligent Systems
Volume12
Issue number1
DOIs
Publication statusPublished - Mar 2022

Keywords

  • Visualization
  • data-driven videos
  • optimization

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