Abstract
Graphic Design (gd) artefacts, like posters on the streets or book covers on store shelves, often compete with each other to be seen, catch attention and communicate effectively. Nevertheless, due to the democratisation of gd and because finding disruptive aesthetics might be time-consuming, graphic designers often follow existing trends, lacking disruptive and catchy visual features. EvoDesigner aims to assist the exploration of distinctive gd aesthetics by employing a genetic algorithm to evolve content within two-dimensional pages. The system takes the form of an extension for Adobe InDesign, so both human designers and the machine can alternately collaborate in the creation process. In this paper, we propose a method to automatically evaluate the generated posters by assessing their dissimilarity to the output of an auto-encoder that was trained with a set of posters posted at typographicposters.com by graphic designers worldwide. The results suggest the viability of the evaluation method to recall large sets of images and therefore be used to compute an image dissimilarity degree. Furthermore, the proposed method could be used for evolving gd posters that can be deemed as new when compared to the training set.
This research was funded by national funds through the fct—Foundation for Science and Technology, i.p., within the scope of the project cisuc—UID/CEC/00326/2020 and by the European Social Fund, through the Regional Operational Program Centro 2020 and is partially supported by Fundação para a Ciência e Tecnologia, under the grant SFRH/BD/143553/2019.
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Lopes, D., Correia, J., Machado, P. (2023). EvoDesigner: Aiding the Exploration of Innovative Graphic Design Solutions. In: Johnson, C., Rodríguez-Fernández, N., Rebelo, S.M. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2023. Lecture Notes in Computer Science, vol 13988. Springer, Cham. https://doi.org/10.1007/978-3-031-29956-8_25
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