BAG: Body-Aligned 3D Wearable Asset Generation.
IEEE Transactions on Visualization and Computer Graphics
|February 16, 2026
Summary
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.
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.
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