Abstract
In this study, we propose a new way to appreciate artwork based on the growing interest in the active appreciation of artwork and the development of machine learning technology. We chose Italian painter Giorgio Morandi, who was active in the first half of the 20 century and known for his unique composition and coloring, as the theme, and developed a hands-on exhibit in which spectators freely arrange and compose objects that reproduce the painter’s motifs, and generates images that reproduce the painter’s coloring by machine learning. From the results of a questionnaire survey of 29 people, we confirmed that experiencing this exhibit deepened their interest in the painter.
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For example, the event “Immersive Museum” which was scheduled to be held in Tokyo from April 2020, projects images of works by impressionist artists such as Degas and Renoir over the entire field of vision so people can enter the world of painting. https://immersive-museum.jp/.
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©DACS, 2020, Photo ©Tate, CC-BY-NC-ND 3.0 (Unported) https://www.tate.org.uk/art/artworks/morandi-still-life-n05782.
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References
Briot, J.-P., Hadjeres, G., Pachet, F.-D.: Deep learning techniques for music generation - a survey (2017)
Deshpande, A., Rock, J., Forsyth, D.: Learning large-scale automatic image colorization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 567–575 (2015)
Elgammal, A.M., Liu, B., Elhoseiny, M., Mazzone, M.: CAN: creative adversarial networks, generating “art” by learning about styles and deviating from style norms. CoRR, abs/1706.07068 (2017). http://arxiv.org/abs/1706.07068
Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. CoRR, abs/1508.06576 (2015). http://arxiv.org/abs/1508.06576
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, pp. 2414–2423 (2016). ISSN 10636919. https://doi.org/10.1109/CVPR.2016.265
Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Kogan, G.: Artist in the cloud: Towards an autonomous artist (2019)
Li-Fen Lilly Lu: A art café: a 3D virtual learning environment for art education. Art Educ. 61(6), 48–54 (2008)
Meyerowitz, J.: Morandi’s Objects. Damiani Editore (2015). ISBN 9788862084536
Meyerowitz, J.: Cézanne’s objects. Damiani Editore (2017). ISBN 9788862085649
Morandi, G.: Giorgio Morandi. Silvana Editoriale (2019)
Salimbeni, G., Leymarie, F.F., Latham, W.: Generative system to assist the artist in the choice of 3D composition for a still life painting. In: Machine Learning for Creativity and Design (NeurIPS 2019 Workshop) (2019)
Di Serio, Á., Ibáñez, M.B., Kloos, C.D.: Impact of an augmented reality system on students’ motivation for a visual art course. Comput. Educ. 68, 586–596 (2013). ISSN 0360–1315. https://doi.org/10.1016/j.compedu.2012.03.002. http://www.sciencedirect.com/science/article/pii/S0360131512000590
Smith, G., Leymarie, F.F.: The machine as artist: an introduction. Arts 6(2), 5 (2017). ISSN 2076–0752. https://doi.org/10.3390/arts6020005. http://www.mdpi.com/2076-0752/6/2/5
Turing, A.M.: Computing machinery and intelligence. Mind 59(236), 433–460 (1950)
Yenawine, P.: Visual Thinking Strategies: Using Art to Deepen Learning Across School Disciplines. Harvard Education Press (2013)
Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
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Kobayashi, S., Kuwakubo, R., Matsui, S., Otani, Y., Zhang, X., Niizumi, D. (2021). The Morandi Room: Entering the World of Morandi’s Paintings Through Machine Learning. In: Yada, K., et al. Advances in Artificial Intelligence. JSAI 2020. Advances in Intelligent Systems and Computing, vol 1357. Springer, Cham. https://doi.org/10.1007/978-3-030-73113-7_13
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