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.

本研究提出一个轻量级神经网络，能够以多个尺度表示不同类型的机织面料，通过编码面料图案和参数为一个小的潜在向量再经由一个小的解码器进行解释而实现。该网络通过像素的足迹作为输入，实现了多尺度表示，其结构轻量且占用存储空间小，能够以接近 60 帧每秒的速度在 RTX 3090 上渲染和编辑机织面料，并且不出现可见的走样和噪声。