Research on the Innovative Application of Song Dynasty Boundary Painting in Interior Soft Decoration Design Based on AIGC

An analysis of the practice path and methodology system of Artificial Intelligence Generated Content (AIGC) technology has been conducted in the field of inheritance and innovation of boundary paintings from Song Dynasty. This paper aims to provide valuable reference and guidance for the application of AI technology in Song Dynasty boundary painting (Song painting) in the interior decoration design, so as to promote the effective integration of traditional aesthetics and modern design concepts. Firstly, the natural processing language model is used to generate the index layer suitable for the indoor soft decoration style of Song paintings, and the Analytic Hierarchy Process weight classification is used to select the cue words of the generated image. Secondly, Midjourney is used to generate Song Dynasty style images for keywords. Finally, Stable Diffusion control model is used to transfer the style of Song painting elements to interior decoration design. AIGC technology can effectively generate images with the style of Song painting elements, and play a unique role in style transfer and pattern design. It provides an innovative path for the integration of traditional art and modern design, and provides a wealth of possibilities for the modern application of Song painting. AIGC technology has significant potential in the inheritance and innovation of Song painting, which can bring new ideas and methods for interior decoration design, and contribute to the wide application and innovative development of Song painting art in the field of modern design.

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Publication Details

Journal
Buildings
Published
2025-03-26
DOI
https://doi.org/10.3390/buildings15071067
Citations
2
Primary Topic
Digital Media and Visual Art
Type
article
Field-Weighted Citation Impact
7.08
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article

Research on the Innovative Application of Song Dynasty Boundary Painting in Interior Soft Decoration Design Based on AIGC

Ziqi Zhang, Xiao Dan Fang, Jian Min Xu, Jingting Meng
2 citations
Buildings
Digital Media and Visual Art
7.08
article

Research on the Innovative Application of Song Dynasty Boundary Painting in Interior Soft Decoration Design Based on AIGC

Ziqi Zhang, Xiao Dan Fang, Jian Min Xu, Jingting Meng
article en
2 citations

Abstract

An analysis of the practice path and methodology system of Artificial Intelligence Generated Content (AIGC) technology has been conducted in the field of inheritance and innovation of boundary paintings from Song Dynasty. This paper aims to provide valuable reference and guidance for the application of AI technology in Song Dynasty boundary painting (Song painting) in the interior decoration design, so as to promote the effective integration of traditional aesthetics and modern design concepts. Firstly, the natural processing language model is used to generate the index layer suitable for the indoor soft decoration style of Song paintings, and the Analytic Hierarchy Process weight classification is used to select the cue words of the generated image. Secondly, Midjourney is used to generate Song Dynasty style images for keywords. Finally, Stable Diffusion control model is used to transfer the style of Song painting elements to interior decoration design. AIGC technology can effectively generate images with the style of Song painting elements, and play a unique role in style transfer and pattern design. It provides an innovative path for the integration of traditional art and modern design, and provides a wealth of possibilities for the modern application of Song painting. AIGC technology has significant potential in the inheritance and innovation of Song painting, which can bring new ideas and methods for interior decoration design, and contribute to the wide application and innovative development of Song painting art in the field of modern design.

BuildingsVol. 15(7)
Shanghai Jiao Tong University (CN), China Jiliang University (CN), South China University of Technology (CN)
Openalex Percentile: Top 4%
Digital Media and Visual Art
7.08
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