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Related Experiment Video

Updated: Oct 20, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Hierarchical Generation of Human Pose With Part-Based Layer Representation.

Xian Wu, Chen Li, Shi-Min Hu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 15, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel human pose transfer method using semantic part-based representations and a hierarchical framework. It improves handling of self-occlusions and detail synthesis for higher-resolution results.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence

    Background:

    • Human pose transfer is an emerging research area with unsatisfactory state-of-the-art results.
    • Existing end-to-end methods lack semantic understanding, leading to limitations.

    Purpose of the Study:

    • To propose a novel human pose transfer method that incorporates semantic part-based representations.
    • To improve the quality and handling of self-occlusions in human pose transfer.

    Main Methods:

    • Segmenting the human body into multiple semantic parts.
    • Utilizing part-based layer generators for high-quality local region results.
    • Designing a three-stage hierarchical framework for coarse-to-fine fusion of local representations.
    • Exploiting spatial guidance from 3D human models to handle self-occlusions.

    Main Results:

    • Achieved superior quantitative and qualitative performance compared to previous approaches.
    • Demonstrated improved handling of self-occlusions, reducing artifacts.
    • Showcased enhanced fine detail preservation and synthesis.
    • Generated higher-resolution human pose transfer results.

    Conclusions:

    • The proposed semantic part-based and spatial-aware approach significantly advances human pose transfer.
    • This method effectively addresses limitations of previous techniques, particularly in complex scenarios like self-occlusions.
    • The hierarchical framework ensures both global consistency and local detail accuracy.