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<p>Along with the flow of clothing designer personnel, clothing style design technology will be lost, so the intelligent design of the clothing industry, with the development of artificial intelligence technology, has received great attention. In this paper, based on the real-time style migration algorithm to study and design the local pattern of clothing, based on the principle of real-time style migration of images with perceptual loss function, it is proposed to optimize the real-time style migration network by using the group normalization method to obtain the style migration image that is more in line with the visual habit. The fusion of the style migration image with the garment base map through Poisson fusion achieves the end-to-end intelligent design of garment local patterns. The AdaIN-based style migration network model designed in this paper has an average accuracy of 75.758% when trained. Then, the model is applied to the clothing fashion design to evaluate the effect of the garment. The average scores of the overall, front, side, and back evaluation of the dressing effect of the garment are 4.04, 4.15, 4.01, and 3.92, respectively. The garments show a better effect. AI technology will bring more innovative possibilities to the clothing fashion industry and inject new vitality into the fashion industry.</p>
</abstract>

Along with the flow of clothing designer personnel, clothing style design technology will be lost, so the intelligent design of the clothing industry, with the development of artificial intelligence technology, has received great attention. In this paper, based on the real-time style migration algorithm to study and design the local pattern of clothing, based on the principle of real-time style migration of images with perceptual loss function, it is proposed to optimize the real-time style migration network by using the group normalization method to obtain the style migration image that is more in line with the visual habit. The fusion of the style migration image with the garment base map through Poisson fusion achieves the end-to-end intelligent design of garment local patterns. The AdaIN-based style migration network model designed in this paper has an average accuracy of 75.758% when trained. Then, the model is applied to the clothing fashion design to evaluate the effect of the garment. The average scores of the overall, front, side, and back evaluation of the dressing effect of the garment are 4.04, 4.15, 4.01, and 3.92, respectively. The garments show a better effect. AI technology will bring more innovative possibilities to the clothing fashion industry and inject new vitality into the fashion industry.

Research on the Future Trends of the Integration of Artificial Intelligence and Fashion Design of Clothing

Shaanxi University of Science and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Along with the flow of clothing designer personnel, clothing style design technology will be lost, so the intelligent design of the clothing industry, with the...

Research on the Future Trends of the Integration of Artificial Intelligence and Fashion Design of Clothing

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 09. Apr. 2024

Akzeptiert: 05. Juli 2024

DOI: https://doi.org/10.2478/amns-2024-2181

Schlüsselwörter
Perceptual loss function, Real-time style migration network, Poisson fusion, Clothing fashion design

© 2024 Ning Sun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Future Trends of the Integration of Artificial Intelligence and Fashion Design of Clothing

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 09. Apr. 2024

Akzeptiert: 05. Juli 2024

DOI: https://doi.org/10.2478/amns-2024-2181

SchlüsselwörterPerceptual loss function, Real-time style migration network, Poisson fusion, Clothing fashion design

© 2024 Ning Sun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Perceptual loss function, Real-time style migration network, Poisson fusion, Clothing fashion design