SHS Web of Conferences (Jan 2024)

Meta Style: The Visual Maze of AIGC Art

  • Gao Jie,
  • Yin Jun

DOI
https://doi.org/10.1051/shsconf/202418301014
Journal volume & issue
Vol. 183
p. 01014

Abstract

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This article aims to explore whether the visual style in AIGC art is constrained by certain factors that hinder the emergence of new styles and to analyze possible paths beyond this limitation. Methodologically, the article first deconstructs and subverts established visual rules in AIGC art from a post-structuralist perspective. Subsequently, it analyzes the limitations that AIGC art currently faces in innovation, namely, excessive reliance on imitating existing styles, from a perspective of technological philosophy. Finally, it proposes breakthroughs in style innovation for AIGC art through diversified training data, human-machine collaboration, and assimilating multicultural resources. The conclusion suggests that there is potential to surpass the 'visual maze' in AIGC art, but it requires progress in various aspects such as data, models, human-machine interaction to achieve this breakthrough. This will not only promote the development of AIGC art itself but also enrich the aesthetic experience of humanity.