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The realistic rendering of woven and knitted fabrics has posed significant challenges throughout many years. Previously, fiber‐based micro‐appearance models have achieved considerable success in attaining high levels of realism. However, rendering such models remains complex due to the intricate internal scatterings of hundreds of fibers within a yarn, requiring vast amounts of memory and time to render. In this paper, we introduce a new framework to capture aggregated appearance by tracing many light paths through the underlying fiber geometry. We then employ lightweight neural networks to accurately model the aggregated BSDF, which allows for the precise modeling of a diverse array of materials while offering substantial improvements in speed and reductions in memory. Furthermore, we introduce a novel importance sampling scheme to further speed up the rate of convergence. We validate the efficacy and versatility of our framework through comparisons with preceding fiber‐based shading models as well as the most recent yarn‐based model. [ABSTRACT FROM AUTHOR]

Realistic cloth rendering is a longstanding challenge in computer graphics due to the intricate geometry and hierarchical structure of cloth: Fibers form plies which in turn are combined into yarns which then are woven or knitted into fabrics. Previous fiber‐based models have achieved high‐quality close‐up rendering, but they suffer from high computational cost, which limits their practicality. In this paper, we propose a novel hierarchical model that analytically aggregates light simulation on the fiber level by building on dual‐scattering theory. Based on this, we can perform an efficient simulation of ply and yarn shading. Compared to previous methods, our approach is faster and uses less memory while preserving a similar accuracy. We demonstrate both through comparison with existing fiber‐based shading models. Our yarn shading model can be applied to curves or surfaces, making it highly versatile for cloth shading. This duality paired with its simplicity and flexibility makes the model particularly useful for film and games production. [ABSTRACT FROM AUTHOR]