AutoClips: An Automatic Approach to Video Generation
from Data Facts


Data videos, a storytelling genre that visualizes data facts with motion graphics, are gaining increasing popularity among data journalists, non-profits, and marketers to communicate data to broad audiences. However, crafting a data video is often time-consuming and asks for various domain knowledge such as data visualization, animation design, and screenwriting. Existing authoring tools usually enable users to edit and compose a set of templates manually, which still cost a lot of human effort.To further lower the barrier of creating data videos, this work introduces a new approach, AutoClips, which can automatically generate data videos given the input of a sequence of data facts. We built AutoClips through two stages. First, we constructed a fact-driven clip library where we mapped ten data facts to potential animated visualizations respectively by analyzing 230 online data videos and conducting interviews. Next, we constructed an algorithm that generates data videos from data facts through three steps: selecting and identifying the optimal clip for each of the data facts, arranging the clips into a coherent video, and optimizing the duration of the video. The results from two user studies indicated that the data videos generated byAutoClips are comprehensible, engaging, and have comparable quality with human-made videos.



Danqing Shi Fuling Sun Xinyue Xu Xingyu Lan David Gotz Nan Cao

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@article{shi2021autoclips, title={AutoClips: An Automatic Approach to Video Generation from Data Facts}, author = {Danqing Shi AND Fuling Sun AND Xinyue Xu AND Xingyu Lan AND David Gotz AND Nan Cao}, journal = {Computer Graphics Forum}, year={2021}, publisher = {The Eurographics Association and John Wiley & Sons Ltd.}, ISSN = {1467-8659}, DOI = {10.1111/cgf.14324} }