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Conditional Image Repainting.

Shuchen Weng, Boxin Shi

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    This summary is machine-generated.

    This study introduces conditional image repainting (CIR), a new image editing task. CIR models synthesize realistic visuals from user-provided conditions, enhancing creative control in digital art.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Digital Image Processing

    Background:

    • Advanced image editing technologies excel at generating visually appealing results based on user input.
    • Existing methods offer limited flexibility in synthesizing complex visual content according to diverse user instructions.

    Purpose of the Study:

    • To introduce and define the conditional image repainting (CIR) task for synthesizing realistic visual content.
    • To develop and evaluate novel CIR models capable of handling multiple cross-modality conditions.
    • To enhance user control over image editing through attribute and language-based color specification.

    Main Methods:

    • Formulated two-phased CIR models as a baseline approach.
    • Designed unified CIR models incorporating novel condition fusion modules for improved performance.
    • Utilized both attribute and natural language inputs for specifying repainted content colors.
    • Collected and processed four distinct datasets to train and validate the models.

    Main Results:

    • Demonstrated the effectiveness of the proposed CIR models across four datasets.
    • Achieved significant improvements in synthesizing realistic visual content based on cross-modality conditions.
    • Showcased the usability and practical applicability of CIR models through various application scenarios.

    Conclusions:

    • Conditional image repainting (CIR) represents a significant advancement in image editing technology.
    • The proposed unified CIR models with condition fusion modules offer enhanced performance and flexibility.
    • CIR models provide a powerful tool for users to express creative intent more freely in image synthesis.