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    This study introduces a new method for controllable image synthesis from spatial layouts and style codes. The approach enables precise control over image generation at both object and image levels.

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    Area of Science:

    • Computer Vision
    • Deep Generative Models
    • Artificial Intelligence

    Background:

    • Deep generative models have advanced significantly, driving interest in controllable image synthesis.
    • The layout-to-image task aims to generate realistic images from structured spatial layouts and style codes.

    Purpose of the Study:

    • To develop a novel generative model for controllable image synthesis from spatial layouts and style codes.
    • To enhance control over image generation at both image and object levels.
    • To propose a weakly-supervised layout-to-mask-to-image paradigm.

    Main Methods:

    • A layout-to-mask-to-image paradigm is proposed, utilizing weakly-supervised object mask unfolding.
    • Generative Adversarial Networks (GANs) are employed for synthesis with layout and style control.
    • A novel Instance-Sensitive and Layout-Aware Normalization (ISLA-Norm) scheme is introduced for enhanced controllability.
    • A semi-supervised version of the method is developed without performance degradation.

    Main Results:

    • The proposed method achieves state-of-the-art performance on the COCO-Stuff and Visual Genome datasets.
    • The ISLA-Norm scheme effectively enables instance-sensitive and layout-aware image synthesis.
    • The layout-to-mask component successfully bridges the gap between input layouts and synthesized images.

    Conclusions:

    • The developed method offers a powerful solution for controllable image synthesis from spatial layouts.
    • The approach demonstrates significant improvements in controlling image generation based on layout and style.
    • The proposed techniques advance the field of generative modeling for structured data inputs.