The apparel industry has witnessed a significant shift toward 3D virtual design, driven by advances in simulation and digitization technologies. Central to this transformation are digital twins, which allow designers to test and refine garment designs virtually, reducing reliance on physical prototyping. A critical aspect of this process is accurately capturing the drape behavior of fabrics, as it impacts both the aesthetic and functional properties of garments. Traditional fabric digitization methods involve mechanical testing of fabric samples, but they are labor-intensive, costly, and require specialized equipment. Recently, AI-based digitization techniques have emerged, offering potential advantages in terms of cost and logistics, though their accuracy remains uncertain. This study addresses the gap in knowledge regarding the accuracy of AI-based fabric digitization by utilizing the Cusick drape test-a standardized method for assessing fabric drape behavior. We introduce a reference digitization pipeline based on traditional mechanical testing and perform a comprehensive comparative study of six commercially available digitization methods, including both traditional and AI-based approaches. Using a diverse set of fabrics, we evaluate the accuracy of the digitized fabrics against real-world measurements. Our results demonstrate that AI-based methods can achieve competitive accuracy, while also highlighting areas for further improvement. We present a publicly available dataset containing real fabric properties, their digitized versions, as well as their corresponding real and digital drape metrics, which can serve as a valuable resource for further research and development in fabric digitization.

This paper introduces a methodology for inverse-modeling of yarn-level mechanics of cloth, based on the mechanical response of fabrics in the real world. We compiled a database from physical tests of several different knitted fabrics used in the textile industry. These data span different types of complex knit patterns, yarn compositions, and fabric finishes, and the results demonstrate diverse physical properties like stiffness, nonlinearity, and anisotropy. We then develop a system for approximating these mechanical responses with yarn-level cloth simulation. To do so, we introduce an efficient pipeline for converting between fabric-level data and yarn-level simulation, including a novel swatch-level approximation for speeding up computation, and some small-but-necessary extensions to yarn-level models used in computer graphics. The dataset used for this paper can be found at http://mslab.es/projects/YarnLevelFabrics.

This paper introduces a method to simulate complex rod assemblies and stacked layers with implicit contact handling, through Eulerian-on-Lagrangian (EoL) discretizations. Previous EoL methods fail to handle such complex situations, due to ubiquitous and intrinsic degeneracies in the contact geometry, which prevent the use of remeshing and make simulations unstable. We propose a novel mixed Eulerian-Lagrangian discretization that supports accurate and efficient contact as in EoL methods, but is transparent to internal rod forces, and hence insensitive to degeneracies. By combining the standard and novel EoL discretizations as appropriate, we derive mixed statics-dynamics equations of motion that can be solved in a unified manner with standard solvers. Our solution is simple and elegant in practice, and produces robust simulations on large-scale scenarios with complex rod arrangements and pervasive degeneracies. We demonstrate our method on multi-layer yarn-level cloth simulations, with implicit handling of both intra-and inter-layer contacts.

Abstract Damping determines how the energy in dynamic deformations is dissipated. The design of damping requires models where the behavior along deformation modes is easily controlled, while other motions are left unaffected. In this paper, we propose a framework for the design of damping using dissipation potentials formulated as functions of strain rate. We study simple parameterizations of the models, the application to continuum and discrete deformation models, and practical implications for implementation. We also study previous simple damping models, in particular we demonstrate limitations of Rayleigh damping. We analyze in detail the application of strain rate dissipation potentials to two highly different deformation models, StVK hyperlasticity and yarn‐level cloth with sliding persistent contacts. These deformation models are representative of the range of applicability of the damping model.