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Related Experiment Video

Updated: Jun 6, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

SegPainter: User-Controllable Face Inpainting via Mask-Aware Semantic Segmentation-Guided Mamba.

Rongji Ke, Degang Chen, Hengyou Wang

    IEEE Transactions on Visualization and Computer Graphics
    |June 4, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    SegPainter enhances face inpainting by adaptively weighting semantic maps, improving results in missing image areas. This Mamba-based architecture offers user-controllable, personalized face editing with superior structural coherence and realism.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Face inpainting is crucial for image editing applications.
    • Existing methods use uniform semantic map weighting, leading to poor results in missing areas.
    • Current approaches lack personalization for user-specific editing.

    Purpose of the Study:

    • To develop a user-controllable face inpainting method.
    • To improve inpainting quality by adaptively using semantic information.
    • To enable personalized face editing based on user preferences.

    Main Methods:

    • Proposed SegPainter, a Mamba-based architecture for face inpainting.
    • Introduced Hard Mask Soft One-Hot Encoding (HMSOE) for adaptive region weighting.
    • Developed Semantic-Guided State Space Model (SG SSM) and Tri-Scan Inspection (TSI) for enhanced guidance and feature capture.

    Related Experiment Videos

    Last Updated: Jun 6, 2026

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    Main Results:

    • SegPainter outperforms state-of-the-art methods on CelebAMask-HQ and FFHQ datasets.
    • Achieved sharper and more semantically consistent face inpainting results.
    • Demonstrated effective user-controllable and personalized restoration.

    Conclusions:

    • SegPainter offers a novel Mamba-based approach for advanced face inpainting.
    • The proposed adaptive weighting and guidance mechanisms significantly improve restoration quality.
    • The framework provides a powerful tool for personalized face editing applications.