Data Pictorial: Deconstructing Raster Images for Data-Aware Animated Vector Posters.
Published in Adjunct Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST Adjunct '24), 2024
Recommended citation: Tongyu Zhou, Gromit Yeuk-Yin Chan, Shunan Guo, Jane Hoffswell, Chang Xiao, Victor S. Bursztyn, and Eunyee Koh. 2024. Data Pictorial: Deconstructing Raster Images for Data-Aware Animated Vector Posters. In Adjunct Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST Adjunct '24). Association for Computing Machinery, New York, NY, USA, Article 98, 1–3. https://doi.org/10.1145/3672539.3686353 https://dl.acm.org/doi/abs/10.1145/3672539.3686353
To support data integration into pictorials, we propose Data Pictorial, a pipeline that deconstructs a raster image into SVG objects whose attributes are contextualized in data. This process is achieved by cropping objects of interest using zero-shot detection, converting them into quantized bitmaps, and tracing the results as SVG paths. The technique then provides suggestions for binding the SVG objects and properties with data fields, affording the flexibility to automatically modify and animate the SVG based on the mapping. The resultant data-aware vector hypermedia can be potential candidates for real-time data inspection and personalization, all while maintaining the aesthetic of the original pictorial.