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Visualizing Visual Adaptation
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Neural Photometry-Guided Visual Attribute Transfer.

Carlos Rodriguez-Pardo, Elena Garces

    IEEE Transactions on Visualization and Computer Graphics
    |December 7, 2021
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    Summary
    This summary is machine-generated.

    This study introduces a deep learning method to transfer visual material attributes to larger areas. The approach is robust to lighting changes and works efficiently for material editing applications.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Material Science

    Background:

    • Propagating visual material attributes like textures to larger samples is challenging.
    • Existing methods often require extensive datasets or are sensitive to variations in illumination and deformation.

    Purpose of the Study:

    • To develop a deep learning method for robustly transferring spatially-varying visual material attributes.
    • To enable efficient material editing applications by generalizing from small samples to larger ones.

    Main Methods:

    • A supervised image-to-image translation framework is employed.
    • Training utilizes multi-illumination images and a data augmentation policy for robustness.
    • The method follows an image analogies approach, requiring similar visual structures in training data.

    Main Results:

    • The method successfully propagates semantic segmentation, normal maps, and image stylizations.
    • It demonstrates robustness to novel illumination conditions and affine deformations.
    • The approach achieves interactive rates, suitable for real-time applications.

    Conclusions:

    • A single material training instance is sufficient for generalization to similar material types.
    • The deep learning method offers an efficient and robust solution for material attribute propagation.
    • This technique reduces the need for massive datasets in material editing and synthesis.