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PictographAI: Interactive Generation of Stylized Pictographs for Presentations

Makarem, Sarah ORCID iD icon; Röddiger, Tobias ORCID iD icon; Riedel, Till ORCID iD icon; Beigl, Michael ORCID iD icon

Abstract:

In today’s data-driven world, effective data visualization is crucial for communication. Recent studies have shown that meaningful and relevant visual embellishments and decorations can significantly enhance data visualization memorability and comprehension. Hence, we introduce PictographAI, a generative tool integrated into presentation software to transform traditional bar charts into Pictographic visualizations. Utilizing a multimodal AI pipeline, PictographAI processes text, images, and raw data from presentation slides to automatically generate contextually appropriate pictographs. Our pipeline uses an Large language model agent, a text-guided image-inpainting model, and algorithmic post-processing to make sense of the slide contents and generate pictographs. As users update their presentation slides, the AI pipeline automates the generation of new pictographs that represent the respective contents. In this work, we demonstrate the concept and working principle that motivates the system architecture and the generative AI pipeline on a bar chart generation use case that integrates into a presentation slide creation workflow.


Originalveröffentlichung
DOI: 10.18420/muc2024-mci-demo-305
Zugehörige Institution(en) am KIT Fakultät für Informatik (INFORMATIK)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2024
Sprache Englisch
Identifikator KITopen-ID: 1000190948
Erschienen in Mensch und Computer 2024 - Workshopband : 01.-04. September 2024, Karlsruhe, Germany
Veranstaltung Mensch und Computer (MuC 2024), Karlsruhe, 01.09.2024 – 04.09.2024
Verlag Gesellschaft für Informatik (GI)
Externe Relationen Abstract/Volltext
Schlagwörter pictographs, generative AI, LLM agent, inpainting, visulization, image generation, presentations
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