Dynamic fabric simulation and rendering algorithms based on reconciling reinforcement learning models

Fabric simulation and virtual display are crucial for realistic applications in fashion design, virtual try on, and digital garment rendering. Traditional methods often face limitations of inefficiency, reduced accuracy, and difficulty in capturing detailed fabric deformations. This study presents a comprehensive framework that integrates a large scale dataset, mesh refinement algorithms, and optimization strategies for fabric modeling. A dataset containing 38,913 two dimensional samples and 6350 three dimensional samples was established using a segmentation algorithm to capture dynamic postures and global shapes with realistic fold deformations. For geometric representation, dynamic triangular meshes were refined through quadratic error measures, Hausdorff distance evaluation, and tensor based local reconstruction, enabling accurate preservation of wrinkles and fine details. To further enhance efficiency, vertex clustering and subdivision algorithms were introduced, combined with strain optimization and Lagrange multiplier based constraint handling. Experimental results show that the proposed approach improves mesh quality, reduces geometric errors, and achieves accurate and efficient virtual fabric deformation. These findings demonstrate strong potential for advancing virtual reality technologies through high fidelity, data driven fabric display solutions.