A Generative Comparative Study of Industrial Design Based on AIGC Tools and Deep Learning

With the rapid advancement of artificial intelligence-generated content (AIGC) technology, the field of industrial design is undergoing unprecedented transformations. By leveraging deep learning algorithms and generative models, AIGC tools can autonomously generate creative design solutions, offerin...

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Vydáno v:2025 IEEE 3rd International Conference on Image Processing and Computer Applications (ICIPCA) s. 1619 - 1622
Hlavní autoři: Jiang, Yuhan, Yao, Xin
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 28.06.2025
Témata:
AIGC
Artificial intelligence
Deep learning
Design tools
generative contrast
Graphics
Image reconstruction
Industrial design
Packaging
Planning
Product design
Product development
Usability
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Shrnutí:With the rapid advancement of artificial intelligence-generated content (AIGC) technology, the field of industrial design is undergoing unprecedented transformations. By leveraging deep learning algorithms and generative models, AIGC tools can autonomously generate creative design solutions, offering designers inspiration and accelerating product development. However, the practical impact of AIGC tools differs significantly from traditional design methods. This study aims to compare and analyze the performance of AIGC tools versus traditional industrial design techniques in the generative design process, with a focus on evaluating differences in creativity, efficiency, design quality, and adaptability. Currently, AIGC tools on the market exhibit notable variations in their generative characteristics, and they have been widely adopted in fields such as product design, architectural planning, packaging design, and graphic design. However, no AIGC tools are specifically tailored for industrial equipment styling. Each AIGC tool operates on distinct algorithms and generative features, resulting in different output when processing the same content. Therefore, selecting high-quality, accurate AIGC design tools for industrial equipment design remains a challenging problem that requires further exploration.
DOI:10.1109/ICIPCA65645.2025.11138555