This paper introduces an innovative approach for capturing data-driven cloth deformation in virtual try-on scenarios. The method involves aligning the garment to a T-posed avatar and generating a comprehensive dataset encompassing both dynamic and static cloth deformation. Leveraging the advancements of this dataset, we propose a novel pipeline to obtain dynamic cloth deformations and intricate cloth wrinkles. Evaluation results, obtained across various garments, highlight the superior performance of our proposed model compared to state-of-the-art methods. Our approach excels in terms of accuracy and speed while preserving a significant advancement in forecasting dynamic cloth wrinkles.