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  1. Home
  2. Low-barrier Dataset Collection With Real Human Body For Interactive Per-garment Virtual Try-on.
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  2. Low-barrier Dataset Collection With Real Human Body For Interactive Per-garment Virtual Try-on.

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Low-Barrier Dataset Collection With Real Human Body for Interactive Per-Garment Virtual Try-On.

Zaiqiang Wu, Yechen Li, Jingyuan Liu

    IEEE Computer Graphics and Applications
    |December 31, 2025

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a cost-effective virtual try-on method using real people for data collection, improving garment alignment and real-time performance for better online shopping experiences.

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction
    • Fashion Technology

    Background:

    • Current image-based virtual try-on (VTON) methods often lack real-time capabilities and are restricted to frontal views.
    • Per-garment VTON methods improve on these limitations but require expensive robotic mannequins and struggle with accurate garment alignment.

    Purpose of the Study:

    • To develop a low-barrier, cost-effective per-garment virtual try-on dataset collection method.
    • To enhance human-garment alignment in synthesized try-on images.
    • To improve the overall quality and temporal consistency of virtual try-on applications.

    Main Methods:

    • Proposed a novel approach for per-garment dataset collection using real human bodies, significantly reducing data acquisition time and cost.
    • Introduced a hybrid person representation to ensure precise alignment between the garment and the human body.
  • Conducted comparative analyses against state-of-the-art image-based virtual try-on methods.
  • Main Results:

    • The proposed method drastically reduces data collection time from 2 hours to 2 minutes per garment.
    • Achieved superior image quality and temporal consistency compared to existing virtual try-on techniques.
    • Demonstrated precise human-garment alignment through the hybrid person representation.

    Conclusions:

    • The developed method offers a practical and efficient solution for creating high-quality virtual try-on datasets.
    • The system effectively supports garment purchasing decisions, enhancing the online shopping experience.
    • This approach overcomes key limitations of previous virtual try-on technologies, paving the way for more accessible and realistic applications.