PictorialAttributes: Depicting Multiple Attributes with Realistic Imaging

Omer Dahary, Min Lu, Or Patashnik, Daniel Cohen-Or

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Traditional visualizations often use abstract graphics, limiting understanding and memorability. Existing methods for pictorial visualization are more engaging, but often create disjointed compositions. To address this, we propose PictorialAttributes, a technique utilizing LLMs and diffusion models to depict data attributes. Examples show its promise for compelling and informative pictorial visualizations.

Original languageEnglish
Title of host publicationProceedings - SIGGRAPH 2024 Posters
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9798400705168
DOIs
StatePublished - 25 Jul 2024
EventSIGGRAPH 2024 Posters - Denver, United States
Duration: 28 Jul 20241 Aug 2024

Publication series

NameProceedings - SIGGRAPH 2024 Posters

Conference

ConferenceSIGGRAPH 2024 Posters
Country/TerritoryUnited States
CityDenver
Period28/07/241/08/24

Funding

FundersFunder number
Deutsch Fund

    Keywords

    • Image Generation
    • Pictorial Visualization

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