Journal of System Simulation
Abstract
Abstract: In order to enhance the realism of garment and human body collision in real-time fabric simulation, an automated human body fitting collision method based on the bounding box and mesh method is proposed. According to the human skeletal structure and garment type, the skeletal information involved in collision simulation is effectively optimized, so as to better obtain the feature points of the human body and semantically segment them. According to the characteristics of skinning animation, simple capsule colliders and mesh colliders are generated to fit the geometric shape of the human body, and the dynamic following of colliders is realized. The generated capsule colliders can realize rough collision instead of the human body and eliminate meshes that are unlikely to collide at distant ends. It achieves accurate collision simulation based on the mesh method and effectively reduces the penetration between the human body and the garment model. Sphere fitting is used at joints where large deformation during bending results in incomplete mesh coverage to avoid local model penetration. The experiment results demonstrate that the proposed method can generate colliders that fit the human body automatically. The method of combining rough collision with a precise collision can not only effectively improve the fidelity of collision simulation between garment and human body but also ensure efficient real-time 3D garment simulation effect.
Recommended Citation
Chen, Yuanyuan; Huai, Yongjian; Nie, Xiaoying; and Lang, Ke
(2023)
"3D Garment Collision Simulation Based on Human Skeletal Features,"
Journal of System Simulation: Vol. 35:
Iss.
9, Article 16.
DOI: 10.16182/j.issn1004731x.joss.22-1406
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss9/16
First Page
2023
Last Page
2034
CLC
TP391.41
Recommended Citation
Chen Yuanyuan, Huai Yongjian, Nie Xiaoying, et al. 3D Garment Collision Simulation Based on Human Skeletal Features[J]. Journal of System Simulation, 2023, 35(9): 2023-2034.
DOI
10.16182/j.issn1004731x.joss.22-1406
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Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons