  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.
