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 or thousands of fibers within a yarn, requiring vast amounts of memory and time to render. In this paper, we introduce a novel framework to capture yarn-level appearance by tracing and aggregating 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 and by replicating various real-world fabrics. Our proposed model's enhanced performance and adaptability make it especially beneficial for film and video game production applications.

翻译：多年来，编织和针织织物的逼真渲染一直面临重大挑战。此前，基于纤维的微观外观模型在实现高真实感方面取得了显著成功。然而，由于纱线内部成百上千根纤维的复杂内散射，渲染此类模型仍然复杂，需要大量的内存和时间。本文提出了一种新框架，通过追踪并聚合穿过底层纤维几何结构的众多光路来捕捉纱线级外观。随后，我们采用轻量级神经网络精确建模聚合的双向散射分布函数（BSDF），这不仅能够准确模拟多种材质，还能显著提升速度并降低内存占用。此外，我们引入了一种新颖的重要性采样方案以进一步加速收敛。通过与先前的基于纤维的着色模型进行对比，并复现多种真实世界织物，我们验证了该框架的有效性和通用性。所提模型增强的性能与适应性使其特别适用于电影和视频游戏制作应用。