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    This study introduces M-R²ET, a novel neural system for skinned motion retargeting. It effectively maps motion between characters with different structures while preserving semantics and geometry.

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    Area of Science:

    • Computer Graphics
    • Artificial Intelligence
    • Animation

    Background:

    • Skinned motion retargeting is challenging due to structural differences between characters.
    • Preserving motion semantics and character geometry is crucial for realistic animation.

    Purpose of the Study:

    • To develop a comprehensive neural system for cross-structure motion retargeting.
    • To address challenges in preserving motion semantics and character geometry during retargeting.

    Main Methods:

    • Propose M-R²ET, a modular neural motion retargeting system.
    • Utilize a cross-structure alignment module for joint correspondences.
    • Employ skeleton-aware and shape-aware residual modification modules.
    • Incorporate distance-based losses for semantics and geometry preservation.
    • Implement a balancing gate for interpolating residual modifications.

    Main Results:

    • M-R²ET learns residual motion modifications in a canonical skeleton space.
    • The system effectively reduces interpenetration and contact-missing.
    • Achieves state-of-the-art performance on the Mixamo dataset.
    • Demonstrates a good balance between motion semantics preservation and geometric accuracy.

    Conclusions:

    • M-R²ET successfully enables cross-structure motion retargeting.
    • The modular approach comprehensively addresses key challenges in motion retargeting.
    • The system offers a robust solution for realistic character animation.