Garments play a ubiquitous role in human life. The field of simulation has developed various methods to replicate their behaviour. However, there remains a gap between research and industry, with information flowing slowly and unevenly. Two of the main discouraging factors in garment simulation are the exploratory work required to integrate new techniques or adapt current ones to the context of realistic garments, and the lack of robustness in most modern methods, particularly those that simulate fabric at a high level of detail. This thesis aims to bridge the gap between commonly used simulation methods in the industry and new, innovative techniques. To achieve this, we conducted an exploratory study on techniques that enhance and expedite the simulation of complex materials, such as fabrics. With regards to the latter, we introduce a new yarn-level bending model that resolves previous robustness issues without introducing any new degrees of freedom in the system. The model is highly tunable and can be used in highly parallel environments.

Digital garments are set to revolutionize the apparel industry in the way we design, produce, market, sell and try-on real garments. But for digital garments to play a central role, from designer to consumer, they must be a faithful digital replica of their real counterpart: a digital twin. Yet, most industry-grade tools used in the apparel industry do not focus on accuracy, but rather on producing fast and plausible drapes for interactive editing and quick feedback, thus limiting the value and the potential of digital garments. The key to accuracy lies in using the proper underlying simulation technology, well documented in the academic literature but historically sidelined in the apparel industry in favor of simulation speed. In this paper, we describe our industry-grade cloth simulation engine, built with a strong focus on accuracy rather than sheer speed. Using a global integration scheme and adopting state of the art simulation practices from the Computer Graphics field, we evaluate a wide range of algorithms to improve its convergence and overall performance. We provide qualitative and quantitative insights on the cost and capabilities of each of these features, with the aim of giving valuable feedback and useful guidelines to practitioners seeking to implement an accurate and robust draping simulator.

To deploy yarn-level cloth simulations in production environments, it is paramount to design very efficient implementations, which mitigate the cost of the extremely high resolution. To this end, nodal discretizations aligned with the regularity of the fabric structure provide an optimal setting for efficient GPU implementations. However, nodal discretizations complicate the design of robust and controllable bending. In this paper, we address this challenge, and propose a model of bending that is both robust and controllable, and employs only nodal degrees of freedom. We extract information of yarn and fabric orientation implicitly from the nodal degrees of freedom, with no need to augment the model explicitly. But most importantly, and unlike previous formulations that use implicit orientations, the computation of bending forces bears no overhead with respect to other nodal forces such as stretch. This is possible by tracking optimal orientations efficiently. We demonstrate the impact of our bending model in examples with controllable anisotropy, as well as ironing, wrinkling, and plasticity.