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Related Experiment Video

Updated: Jun 4, 2026

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

MaPPA: Multimodal Controllable Person Image Generation With Pose and Appearance Guidance.

Mang Ye, Yuran Dong, Tongxin Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 2, 2026
    PubMed
    Summary

    This study introduces MaPPA, a multimodal framework for person image generation, enabling flexible control using text, pose, and appearance guidance. MaPPA offers enhanced controllability and detail preservation for realistic virtual try-on and content creation applications.

    Related Experiment Videos

    Last Updated: Jun 4, 2026

    Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
    06:53

    Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

    Published on: March 1, 2017

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Current person image generation models face limitations due to rigid modality requirements, restricting flexibility and controllability.
    • Existing pipelines often rely on fixed input patterns (e.g., pose-plus-appearance or text-only), hindering practical applications requiring mixed guidance.

    Purpose of the Study:

    • To propose MaPPA (Multimodal Controllable Person Image Generation with Pose and Appearance Guidance), a unified framework for flexible and controllable person image synthesis.
    • To enable generation conditioned on arbitrary combinations of text, appearance images, and pose keypoints within a single pipeline.

    Main Methods:

    • Developed a unified multimodal framework leveraging transformer-based latent diffusion models.
    • Introduced dedicated control blocks for appearance and pose guidance, integrated with transformer base blocks.
    • Implemented a cumulative classifier-free guidance strategy for independent adjustment of appearance and pose guidance strengths.
    • Proposed a texture enhancement decoding (TED) strategy to improve detail preservation in generated images.

    Main Results:

    • MaPPA achieves competitive quantitative scores and superior perceptual quality compared to existing methods.
    • The framework demonstrates enhanced controllability, allowing fine-grained adjustment of guidance modalities.
    • The texture enhancement decoding strategy effectively alleviates texture distortion and preserves high-frequency details.

    Conclusions:

    • MaPPA offers a flexible and practical solution for multimodal person image generation, overcoming limitations of previous approaches.
    • The unified pipeline supports combinations of text, pose, and appearance modalities, demonstrating significant practical value.
    • The proposed methods enhance controllability and image quality, making it suitable for diverse applications like virtual try-on and digital content creation.