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Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training
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BAG: Body-Aligned 3D Wearable Asset Generation.

Zhongjin Luo, Yang Li, Mingrui Zhang

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    This summary is machine-generated.

    We introduce Body Aligned Asset Generation (BAG), a novel method for creating 3D wearable assets that automatically fit 3D human bodies. This approach ensures accurate and diverse asset generation for virtual applications.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Artificial Intelligence

    Background:

    • General 3D shape generation has advanced significantly.
    • Automatic generation and fitting of wearable 3D assets remain challenging.

    Purpose of the Study:

    • To present a method for generating 3D wearable assets that automatically fit 3D human bodies.
    • To control the 3D asset generation process using human body shape and pose information.

    Main Methods:

    • Constructed a single-image-to-consistent-multi-view diffusion model trained on Objaverse.
    • Developed a body-conditioned multi-view ControlNet using 2D body projections as control signals.
    • Reconstructed 3D asset shape using a native 3D diffusion model and refined fitting with silhouette supervision and physics simulation.

    Main Results:

    • The method generates body-aligned multi-view images guided by human body shape and pose.
    • Achieved accurate asset fitting, mitigating penetration issues.
    • Significantly outperformed existing methods in prompt adherence, shape diversity, and quality.

    Conclusions:

    • BAG effectively addresses the gap in automatic wearable 3D asset generation.
    • The proposed approach enables precise fitting of generated assets onto target 3D human bodies.
    • Demonstrated superior performance in generating diverse, high-quality, and well-fitting 3D assets.