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    This study introduces a new model for generating realistic two-person interactions from text, significantly reducing mesh penetrations and improving contact realism by considering body shape and contact dynamics.

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    Area of Science:

    • Computer Vision
    • Computer Animation
    • Machine Learning

    Background:

    • Existing latent motion diffusion models struggle with physically plausible contacts and diverse body shapes, leading to unrealistic two-person interactions.
    • Mesh penetrations and lack of interaction realism are common issues in current text-to-motion generation methods.
    • Addressing these limitations is crucial for advancing realistic character animation and virtual environment interactions.

    Purpose of the Study:

    • To propose a novel contact and shape-aware latent motion representation and diffusion model (CoShMDM) for generating realistic two-person interactions from text.
    • To enhance the physical plausibility of generated motions by accurately capturing fine-grained mesh-level contacts and minimizing mesh penetrations.
    • To accommodate body shape diversity and ensure coherent, realistic interactions across various human forms.

    Main Methods:

    • Constructing contact-compatible motion using SMPL-based meshes and a normal alignment-based mesh contact matrix.
    • Incorporating SMPL shape parameters to learn contact dynamics across different body shapes and employing a reinforcement learning-based policy for mesh penetration avoidance.
    • Utilizing a dual-encoder VQ-VAE for disentangled latent representations and a diffusion transformer with a novel contact and motion consistency module.

    Main Results:

    • The proposed CoShMDM model achieved state-of-the-art performance on the InterHuman and InterX datasets, evidenced by low FID scores (4.801 and 0.013).
    • Demonstrated significant reductions in mesh penetrations by 19% and 17.3% compared to existing methods.
    • Achieved substantial gains in contact similarity, with improvements of 17.8% and 33.2% respectively.

    Conclusions:

    • The CoShMDM framework effectively generates realistic and physically plausible two-person interactions from text, addressing key limitations of prior work.
    • The integration of contact awareness, shape diversity, and penetration avoidance significantly enhances the quality and realism of generated motion sequences.
    • This research offers a promising advancement for applications in computer animation, virtual reality, and human-computer interaction.