New Trends and Industry Impacts of Digital Art Creation Driven by AIGC
Journal: Arts Studies and Criticism DOI: 10.32629/asc.v6i6.4734
Abstract
In recent years, AIGC technology has made a major breakthrough, deeply integrated into digital art creation and changing the development pattern of the industry. It gives rise to two main trends, the transformation of creative tools towards intelligence and cooperation, making the process simple and stimulating creativity with functions such as natural language interaction; the creative subject is transformed from elite to popular, with more than 70% of creators are non-professionals, promoting the popularization of art. At the industry level, AIGC reconfigures the creative model and the division of labor. A human-machine team has a shorter cycle, a higher degree of creativity. At the same time, it promotes the transformation of talent demand. compound talents are becoming scarce resources.
Keywords
AIGC, digital art creation, intelligent creative tools, multidisciplinary talent
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[1] Ren, Q., Tang, Y., & Lin, Y. (2024). Digital art creation under AIGC technology innovation: multidimensional challenges and reflections on design practice, creation environment and artistic ecology. Computers and Artificial Intelligence, 1(1), 1-12.
[2] Mou, L., Gao, F., Li, Z., Liu, J., Yao, H., & Hoorn, J. F. (2024). Editorial for special issue on artificial intelligence for art. Machine Intelligence Research(001), 021.
[3] Zhao, P., & Shen, L. (2024). Research on AIGC-driven innovation path and smart creative workflow optimization in design field. Applied Mathematics and Nonlinear Sciences, 9(1).
[4] Wang, Y., & Zhou, Y. (2024). Research on the mechanisms of the digital art talent cultivation mode in colleges and universities based on the AIGC. Journal of Educational Research and Policies, 6(11), 89-94.
[2] Mou, L., Gao, F., Li, Z., Liu, J., Yao, H., & Hoorn, J. F. (2024). Editorial for special issue on artificial intelligence for art. Machine Intelligence Research(001), 021.
[3] Zhao, P., & Shen, L. (2024). Research on AIGC-driven innovation path and smart creative workflow optimization in design field. Applied Mathematics and Nonlinear Sciences, 9(1).
[4] Wang, Y., & Zhou, Y. (2024). Research on the mechanisms of the digital art talent cultivation mode in colleges and universities based on the AIGC. Journal of Educational Research and Policies, 6(11), 89-94.
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