Abstract
Mobile photography is the most commonly used way for people to record images of their lives and work. As AI technology advances, specifically in neural style transfer and large-scale multi-modal models, AIGC (Artificial Intelligent Generated Content) provides a new possibility for the evolution of mobile photography. Through analyzing the habits of ordinary people and professional photographers in taking and editing mobile photos, we investigated how AI can help to turn professionals’ best practice into standard assistance for ordinary people, effectively improving the quality and efficiency. Through analyzing the visual elements and structure of AIGC, we found how to decompose and reconstruct a style in the aspects of form, color and detail such as line and texture. We found how to fuse AIGC and a photo by blending stylized images and the original photo in different ways for different themes. These specific methods can be summarized into corresponding replicable models and processes, rather than artistically inspired creations, making the automatic implementation possible.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ling, R., Fortunati, L., Goggin, G., Lim, S.S., Li, Y.: The Oxford Handbook of Mobile Communication and Society, p. 392 (2020)
Fatima, N.: AI in photography: scrutinizing implementation of super-resolution techniques in photo-editors. In: 35th International Conference on Image and Vision Computing New Zealand, pp. 1–6. Wellington, New Zealand (2020)
Mazzone, M., Elgammal, A.: Art, creativity, and the potential of artificial intelligence. Arts 8(1), 26 (2019)
Cetinic, E., She, J.: Understanding and creating art with ai: review and outlook. ACM Trans. Multimedia Comput. Commun. Appl. 18(2), 1–22 (2022)
Wu, Z., Ji, D., Yu, K., Zeng, X., Wu, D., Shidujaman, M.: AI creativity and the human-AI co-creation model. In: Kurosu, M. (ed.) HCII 2021. LNCS, vol. 12762, pp. 171–190. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78462-1_13
Daniele A., Song, Y.: AI + Art = Human. In: AAAI/ACM Conference on AI, Ethics, and Society (AIES 2019) on Proceedings, pp. 155–161. New York, USA (2019)
Mikalonytė, E., Kneer, M.: Can artificial intelligence make art?: folk intuitions as to whether ai-driven robots can be viewed as artists and produce art. ACM Trans. Hum. Robot Interact. 11(4), 1–19 (2022)
Manovich, L.: Designing and Living Instagram Photography: Themes, Feeds, Sequences, Branding, Faces, Bodies. Instagram and Contem-porary Image, Chapter 4 (2016)
Top themes of photos by hashtag on Instagram. https://instagram.com/. Accessed 8 Dec 2022
The annual winners at Mobile Photography Awards 2017–2021. https://mobilephotoawards.com/11th-annual-mpa-winners-honorable-mentions/. Accessed 8 Dec 2022
The annual winners at iPhone Photography Awards 2017–2021. https://www.ippawards.com/gallery/?v=1c2903397d88Online. Accessed 8 Dec 2022
Farhat, F., Kamani, M. M., Wang, J. Z.: CAPTAIN: comprehensive composition assistance for photo taking. ACM Trans. Multimedia Comput. Commun. Appl. 18(1), 1–24 (2022)
Wu, Z., et al.: Human-AI co-creation of art based on the personalization of collective memory. In: ACAIT 2022 on Proceedings. Changzhou, China (2022)
Das, P., Varshney, L.R.: Explaining artificial intelligence generation and creativity. IEEE Sign. Process. Magaz. 39(4), 85–95 (2022)
VISUAL FX & DIGITAL ART WINNERS | 11TH ANNUAL MPA, https://mobilephotoawards.com/visual-fx-digital-art-winners-11th-annual-mpa/. Accessed 8 Dec 2022
Acknowledgements
The authors like to thank Wenrui Liao, Yuan Zhang, Gongkai Luo, Yongkang Lin, Zhiting He, Shenglin Xu, Gen Mai, Dan Wu, Congyu Jiang, Tianpei Zang, Xiangtan Zhao and all the volunteers for their contribution in this paper, as well as Liwen Feng for the technical support on deploying and optimizing AI applications. This work was also supported by the Beijing Nova Program (Z211100002121160).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, Z., Fan, M., Tang, R., Ji, D., Shidujaman, M. (2023). The Art of Artificial Intelligent Generated Content for Mobile Photography. In: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2023. Lecture Notes in Computer Science, vol 14014. Springer, Cham. https://doi.org/10.1007/978-3-031-35572-1_29
Download citation
DOI: https://doi.org/10.1007/978-3-031-35572-1_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35571-4
Online ISBN: 978-3-031-35572-1
eBook Packages: Computer ScienceComputer Science (R0)