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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Text-based image manipulation offers flexibility but faces challenges with spatial inaccuracy, appearance ambiguity, and incomplete descriptions.
    • Current methods struggle to precisely control human image editing based on textual instructions.

    Purpose of the Study:

    • To develop a novel method for accurate and interactive human image manipulation guided by text.
    • To address the limitations of spatial inaccuracy, ambiguity, and incompleteness in text-guided image editing.

    Main Methods:

    • Utilized structured information, such as poses, to improve spatial accuracy by disentangling appearance and spatial control.
    • Learned a shared image-text space with disentanglement to separate relevant and irrelevant editing directions.
    • Generated sequential manipulation results for manual selection to reduce ambiguity.
    • Proposed an efficient pseudo-label loss to enhance performance with incomplete text.

    Main Results:

    • The proposed method demonstrates improved precision and interactiveness in human image manipulation.
    • Disentangling spatial structure and appearance control enhances the accuracy of text-guided edits.
    • Sequential output generation effectively reduces ambiguity in textual descriptions.

    Conclusions:

    • The developed method offers a robust solution for text-guided human image manipulation, overcoming key limitations of existing approaches.
    • The approach provides precise control and interactive selection, leading to higher quality image edits.