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Image Comes Dancing With Collaborative Parsing-Flow Video Synthesis.

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    Summary
    This summary is machine-generated.

    This study introduces the Collaborative Parsing-Flow Network (CPF-Net) for realistic human motion transfer. The novel method enables transferring motion to any target person using just one image, improving scalability and visual fidelity.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Human Motion Analysis

    Background:

    • Human motion transfer is crucial for computer vision and graphics.
    • Existing methods lack scalability, relying on 3D models or per-person training.
    • Transferring motion while preserving appearance is challenging with limited target information.

    Purpose of the Study:

    • To develop a scalable, single model for human motion transfer to any target person.
    • To faithfully preserve target person's appearance during pose transformation.
    • To generate realistic and temporally coherent target motion videos.

    Main Methods:

    • Introduced the Collaborative Parsing-Flow Network (CPF-Net).
    • Integrated structured human parsing and appearance flow for foreground synthesis.
    • Employed a spatio-temporal fusion module for background integration.
    • Decoupled the process into parsing generation, foreground generation, and video generation stages.

    Main Results:

    • CPF-Net effectively preserves appearance during motion transfer.
    • Generated videos exhibit realistic appearance and temporal coherence.
    • The method significantly outperforms previous approaches in quantitative and qualitative evaluations.
    • A new dataset of human dancing videos was collected to advance research.

    Conclusions:

    • CPF-Net offers a scalable and effective solution for human motion transfer.
    • The approach successfully generates photo-realistic target videos from single images.
    • The integrated human parsing and appearance flow are key to realistic synthesis.