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PACE: Data-Driven Virtual Agent Interaction in Dense and Cluttered Environments.

James F Mullen, Dinesh Manocha

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    Summary
    This summary is machine-generated.

    We developed PACE, a new method to modify virtual agent motions for realistic interaction in complex 3D environments. This technique optimizes agent movement to naturally navigate and engage with scene elements, enhancing virtual agent believability.

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    Area of Science:

    • Computer Graphics
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Virtual agents require realistic motion for believable interaction.
    • Existing methods struggle with dynamic navigation in cluttered 3D environments.

    Purpose of the Study:

    • To present PACE, a novel method for adapting motion-captured virtual agents.
    • To enable agents to interact with and move through dense, cluttered 3D scenes.

    Main Methods:

    • Identifies key motion frames and pairs them with scene geometry, obstacles, and semantics.
    • Optimizes agent motion by altering high-degrees-of-freedom poses per frame.
    • Employs novel loss functions to ensure realistic motion flow and naturalness.

    Main Results:

    • Human evaluators preferred PACE over state-of-the-art methods (57.1% vs. existing motions, 81.0% vs. synthesis).
    • PACE significantly improved physical plausibility metrics, outperforming competitors in non-collision (1.2%) and contact (18%).

    Conclusions:

    • PACE effectively modifies virtual agent motion for realistic interaction in complex scenes.
    • The method enhances agent believability and navigability, validated by user studies and metrics.