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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Spatial-temporal Relation guided Motion Transfer via Diffusion Model.

Yuan Li, Jian Wu, Runze Fan

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    |June 23, 2026
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    Summary
    This summary is machine-generated.

    This study introduces a novel framework for transferring human-object interaction (HOI) motion to new objects. The method enhances motion transfer quality and stability by considering spatial-temporal relationships.

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    Area of Science:

    • Robotics and Virtual Reality
    • Computer Vision
    • Human-Computer Interaction

    Background:

    • Existing methods for Human-Object Interaction (HOI) motion transfer primarily focus on surface correspondences.
    • These traditional approaches neglect crucial internal topological structures of humans and objects, as well as non-surface spatial and temporal relationships.
    • This limitation hinders the accurate and adaptable transfer of HOI motions to novel objects.

    Purpose of the Study:

    • To develop an advanced framework for transferring HOI motion to novel objects.
    • To address the limitations of existing methods by incorporating internal topologies and temporal dynamics.
    • To generate spatially, semantically, and temporally consistent HOI motions adapted to new object geometries.

    Main Methods:

    • Introduction of a spatial-temporal relation interaction graph representation (STRIG) to model human and object internal topologies, global human-object topology, and temporal motion relations.
    • Development of a STRIGs-guided motion transfer diffusion model for generating HOI motions.
    • Implementation of a spatial-temporal relation optimization strategy to address the lack of ground-truth data in motion transfer.

    Main Results:

    • The proposed method demonstrates superior performance in motion transfer quality, efficiency, and sequence stability compared to existing approaches.
    • The framework exhibits robustness, particularly when dealing with target objects possessing significant topological variations.
    • Generated motions are spatially, semantically, and temporally consistent with the interaction context.

    Conclusions:

    • The spatial-temporal relation guided motion transfer framework offers a significant advancement in HOI motion adaptation.
    • STRIGs provide a comprehensive representation for capturing complex interaction dynamics.
    • The diffusion model and optimization strategy effectively generate high-quality, stable, and robust HOI motions for novel objects.