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Conference object . 2025
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Article . 2025
License: CC BY
Data sources: Datacite
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Article . 2025
License: CC BY
Data sources: Datacite
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Poster Generation for Events Using Generative AI

Authors: Siby, Alen; Devis, Jinson;

Poster Generation for Events Using Generative AI

Abstract

The rapid advancement of generative artificial intelligence (AI) has unlocked new possibilities across various industries, particularly in creative design. One of the most promising applications is automated event poster creation, where AI-powered systems streamline the traditionally time-consuming design process. By leveraging techniques like Generative Adversarial Networks (GANs) and transformer-based models, AI can generate high-quality, visually appealing posters based on user inputs such as event details, themes, and branding preferences. This approach reduces reliance on human expertise while ensuring customization and personalization, making professional design accessible to individuals and businesses with limited resources Our proposed system embeds generative AI into event management platforms which will aid in optimizing efficiency, scalability, and cost efficiency. As with any application, questions remain regarding issues of data bias, copyright, and user experience adjustments. Exciting trends will undoubtedly occur, with advances in AI-assisted trend analysis, dynamic video posters, and augmented reality (AR). Generative AI can automate and improve the poster creation process, and by doing so, it can assist in enhancing the event marketing process in more intelligent, intuitive, and inclusive ways.

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green