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Knit-Pix2Pix: An Enhanced Pix2Pix Network for Weft-Knitted Fabric Texture Generation.

Xin Ru1,2,3, Yingjie Huang1, Laihu Peng1

  • 1College of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
Summary

Knit-Pix2Pix generates realistic weft-knitted fabric textures from unit mesh maps, overcoming distortions from traditional methods. This framework enhances virtual try-on and digital textile design with improved visual accuracy.

Keywords:
fabric simulationgenerative adversarial networkstexture mappingweft-knitted fabrics

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Area of Science:

  • Computer Vision
  • Digital Textiles
  • Material Science

Background:

  • Traditional texture mapping for weft-knitted fabrics struggles with visual distortions due to overlooked yarn variations during stretching.
  • Existing methods rely on geometric transformations, failing to capture complex loop morphology changes in real-time applications like virtual try-on.

Purpose of the Study:

  • To introduce Knit-Pix2Pix, a novel framework for generating realistic weft-knitted fabric textures directly from knitted unit mesh maps.
  • To address the limitations of conventional texture mapping by accounting for multi-scale features and deformation awareness.

Main Methods:

  • Developed Knit-Pix2Pix, an integrated architecture featuring multi-scale feature extraction, a grid-guided attention mechanism, and a multi-scale discriminator.
  • Utilized knitted unit mesh maps representing loop deformation states for texture generation.
  • Created a dataset of over 2000 fabric stretching image and mesh map pairs, validated with spring-mass fabric simulations.

Main Results:

  • Knit-Pix2Pix significantly improved texture realism compared to traditional methods.
  • Quantitative metrics showed a 21.8% increase in SSIM, a 20.9% increase in PSNR, and a 24.3% decrease in LPIPS.
  • The framework effectively captures multi-scale features and deformation-aware requirements for weft-knitted fabrics.

Conclusions:

  • Knit-Pix2Pix offers a practical and effective solution for generating high-fidelity weft-knitted fabric textures.
  • The proposed method enhances the visual accuracy required for advanced digital textile design and virtual try-on applications.
  • This approach represents a significant advancement in real-time fabric texture synthesis for deformed surfaces.