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    This study introduces a deep learning pipeline to fix perspective distortion in close-up portraits. The method effectively rectifies facial images, significantly improving quality and speed compared to existing techniques.

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    Area of Science:

    • Computer Vision
    • Deep Learning
    • Image Processing

    Background:

    • Close-up portraits and selfies often exhibit perspective distortion.
    • Existing methods for distortion correction can be complex and time-consuming.

    Purpose of the Study:

    • To develop an end-to-end deep learning pipeline for mitigating perspective distortion in facial images.
    • To improve the quality and efficiency of image rectification for portraits.

    Main Methods:

    • A deep convolutional neural network (CNN) predicts facial depth for perspective adjustment.
    • A differentiable renderer facilitates end-to-end training of depth estimation and feature extraction.
    • An inpainting module reconstructs missing pixels, aided by a camera movement prediction module.

    Main Results:

    • The proposed pipeline effectively rectifies perspective distortion in full-frame images.
    • It outperforms previous methods in both quantitative and qualitative evaluations.
    • Achieves comparable results to 3D GAN-based methods but is over 260 times faster.

    Conclusions:

    • The deep learning pipeline offers a fast and effective solution for perspective distortion correction in portraits.
    • Processing full-frame images simplifies the rectification process, eliminating complex post-processing.
    • The use of synthetic data generated with Unreal Engine demonstrates the robustness of the approach.