Verma, ShashikantShashikantVermaRaman, ShanmuganathanShanmuganathanRaman2025-10-132025-10-132025-09-1410.1109/ICIP55913.2025.110845112-s2.0-105028571813https://repository.iitgn.ac.in0/handle/IITG2025/33148Accurate 3D modeling of humans and high-fidelity garments is crucial in computer vision and graphics, impacting gaming, virtual, and augmented reality applications. While recent data-driven approaches have progressed in estimating segregated geometries for clothed humans, they often struggle with the seamless integration required for physics-based simulations. We introduce Deformable Animation Ready Templates (DARTs) to address these challenges, which enhance template-based garment reconstruction. Our framework employs a robust feature-line regressor network to establish precise deformation constraints guided by input image characteristics. Additionally, we present a novel differentiable Constrained Rigid Deformation Layer (CRDL) that facilitates effective template deformation while preserving the essential geometry of the garment. Our experiments demonstrate that DARTs can generate templates for physics-based simulation, allowing for seamless garment animations influenced by dynamic environmental factors. With minor adjustments, our templates can accommodate various clothing categories, promoting diversity in animated garment modeling.en-US3D Garment reconstructionTemplate-based clothingDeep learningConference Paper0123456789/493