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Image Localized Style Transfer to Design Clothes Based on CNN and Interactive Segmentation.

Hanying Wang1, Haitao Xiong2, Yuanyuan Cai1

  • 1School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China.

Computational Intelligence and Neuroscience
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Summary
This summary is machine-generated.

This study introduces an interactive method for localized clothing style transfer using deep learning. The approach enables precise control over style application to specific garments while perfectly preserving their original shape.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Digital Image Processing

Background:

  • Deep learning has advanced image style transfer.
  • Existing methods lack user control for localized clothing style transfer.
  • Current techniques struggle with preserving the precise shape of garments.

Purpose of the Study:

  • To propose an interactive method for localized clothing style transfer.
  • To enable users to control the specific regions for style application.
  • To ensure perfect preservation of clothing shape during style transfer.

Main Methods:

  • Introduced an interactive algorithm to extract an 'outline image' of the desired clothing.
  • Developed an outline loss function utilizing distance transform for shape preservation.
  • Employed total variation regularization for boundary smoothing and denoising.
  • Constrained style generation to the selected clothing area, excluding the background.

Main Results:

  • Achieved perfect preservation of original clothing shapes.
  • Generated impressive and visually appealing clothing images with new styles.
  • Demonstrated effective localized style transfer on clothing items.
  • Successfully prevented style bleeding into the background.

Conclusions:

  • The proposed interactive method offers precise control over localized clothing style transfer.
  • The technique successfully preserves garment shape and enhances design possibilities.
  • This approach represents a significant improvement for digital clothing design and style manipulation.