Abstract
Lamuse is a joint project between artists and Machine Learning academic scholars. It aims at building pictorial compositions in order to provide sources of inspiration and assist painters in their process of creation. It relies on Artificial Intelligence, mainly based on various artificial neural networks, used for object recognition and style transfer. This article presents how, with minimal effort and without requiring extensive computational power Lamuse can take into account the visual universe of a painter, their artistic references, personal inspiration sources and preferred visual code books to create suggestions of painting subjects the human artist can then use as a source of inspiration for actual creation. Code developed in this project is Open Source and a free-to-use demonstration website is publicly accessible.
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Notes
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Solo show exhibition, “Jours de lune” gallery, Metz, France, 2014. https://www.emmanuellepotier.com/expo-jours-de-lune.
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We are definitely not making the statement there is such a thing as “good” art or that there are explicit rules that would actually define it. However, we are making the assumption that, when a painter is considering starting a new project, there may be a class of existing inspirational paintings sharing a number of inherent properties (composition, color, texture ...) that confer them a particular subjective interest at that point in time and for that specific artist.
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Acknowledgment
The authors want to thank all the students who contributed to developing and enhancing the software that made all this possible: M. Fouques, F. Abouda, E. Dargent, J. Levarlet, F. Amathieu, B. Camus, T. Fontenit, A. Guyot and Y. Petit.
Many thanks to the artists who have accepted giving their opinion on Lamuse.
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Lamiroy, B., Potier, E. (2022). Lamuse: Leveraging Artificial Intelligence for Sparking Inspiration. In: Martins, T., Rodríguez-Fernández, N., Rebelo, S.M. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2022. Lecture Notes in Computer Science, vol 13221. Springer, Cham. https://doi.org/10.1007/978-3-031-03789-4_10
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