Accurate 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.