  Woven fabrics are widely used in applications of realistic rendering, where
real-time capability is also essential. However, rendering realistic woven
fabrics in real time is challenging due to their complex structure and optical
appearance, which cause aliasing and noise without many samples. The core of
this issue is a multi-scale representation of the fabric shading model, which
allows for a fast range query. Some previous neural methods deal with the issue
at the cost of training on each material, which limits their practicality. In
this paper, we propose a lightweight neural network to represent different
types of woven fabrics at different scales. Thanks to the regularity and
repetitiveness of woven fabric patterns, our network can encode fabric patterns
and parameters as a small latent vector, which is later interpreted by a small
decoder, enabling the representation of different types of fabrics. By applying
the pixel's footprint as input, our network achieves multi-scale
representation. Moreover, our network is fast and occupies little storage
because of its lightweight structure. As a result, our method achieves
rendering and editing woven fabrics at nearly 60 frames per second on an RTX
3090, showing a quality close to the ground truth and being free from visible
aliasing and noise.
