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Would Your Clothes Look Good on Me? Towards Transferring Clothing Styles with Adaptive Instance Normalization.

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  • 1Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy.

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Summary

This study introduces a novel deep learning method for transferring clothing styles between individuals, enhancing fashion design creativity. The adapted StarGANv2 architecture effectively generates new fashion images by transferring styles.

Keywords:
deep learningstyle transfer

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Fashion Technology

Background:

  • Deep learning applications in fashion, including image synthesis, are gaining traction.
  • Generating realistic clothing images is a complex, unsolved challenge with significant creative potential for designers.

Purpose of the Study:

  • To address the challenge of style transfer for clothing images between different individuals.
  • To adapt and enhance the StarGANv2 architecture for fashion-specific style transfer applications.

Main Methods:

  • Modified the StarGANv2 architecture to eliminate the need for distinct domain separation.
  • Incorporated a perceptual loss function to improve the accuracy of style transfer between target and source clothing.
  • Edited the style encoder to better capture and represent the stylistic features of target clothing.

Main Results:

  • Demonstrated the efficacy of the proposed method through qualitative and quantitative experiments on the DeepFashion2 dataset.
  • Validated the novelty of the adapted architecture for fashion image style transfer.
  • Achieved successful style transfer of clothing between different individuals, enhancing creative possibilities.

Conclusions:

  • The adapted StarGANv2 architecture offers a promising solution for clothing style transfer in the fashion industry.
  • The method enhances designer creativity by enabling novel fashion image generation.
  • Further research can explore advanced style manipulation and personalization in fashion AI.