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    This study introduces a novel pose flow learning scheme for generating images in new poses while retaining appearance details. The method effectively handles complex deformations and occlusions, outperforming existing techniques.

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    Area of Science:

    • Computer Vision
    • Image Synthesis
    • Machine Learning

    Background:

    • Pose guided synthesis aims to create images in target poses, preserving source image details.
    • Current methods struggle with non-rigid deformations and occlusions, limiting appearance preservation.

    Purpose of the Study:

    • To develop a pose flow learning scheme for appearance detail transfer without annotated correspondences.
    • To introduce GarmentNet and SynthesisNet for coarse-to-fine image synthesis using multi-scale feature alignment.

    Main Methods:

    • Pose flow learning to transfer appearance details.
    • Multi-scale feature-domain alignment for synthesis.
    • Development of GarmentNet and SynthesisNet models.

    Main Results:

    • The proposed approach effectively preserves appearance details across different poses.
    • Demonstrated superior performance compared to state-of-the-art methods on benchmark datasets.
    • Showcased generalization to unseen poses and clothing styles.

    Conclusions:

    • The pose flow learning scheme offers a robust solution for pose guided synthesis.
    • GarmentNet and SynthesisNet provide effective coarse-to-fine synthesis capabilities.
    • The method advances the field of image generation with improved appearance preservation.