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Enhancing Photorealism Enhancement.

Stephan R Richter, Hassan Abu Alhaija, Vladlen Koltun

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 12, 2022
    PubMed
    Summary

    We developed a new method using convolutional networks to improve synthetic image realism. Our approach addresses dataset differences and uses novel training strategies for better results.

    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • Synthetic image generation often suffers from artifacts and lack of realism.
    • Existing methods struggle with variations in scene layout distributions across datasets.
    • Deep learning, particularly convolutional networks, shows promise for image enhancement.

    Purpose of the Study:

    • To enhance the photorealism of synthetic images.
    • To address artifacts caused by dataset scene layout discrepancies.
    • To improve the stability and realism of enhanced synthetic images.

    Main Methods:

    • Utilized a convolutional network leveraging intermediate rendering pipeline representations.
    • Employed a novel adversarial training objective for multi-level perceptual supervision.

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  • Introduced a new image patch sampling strategy and architectural improvements for deep network modules.

    • Main Results:

    • Achieved substantial gains in stability and realism compared to existing methods.
    • Demonstrated effectiveness in overcoming artifacts related to scene layout variations.
    • Outperformed recent image-to-image translation methods and other baselines.

    Conclusions:

    • The proposed approach significantly enhances synthetic image realism.
    • The novel training strategy and architectural improvements are key to the success.
    • This method offers a robust solution for generating high-fidelity synthetic images.