> As for NeuMIP, it can produce results with a higher quality than ours in terms of MSE, at the cost of a higher storage and per-material training. > Furthermo

> As for NeuMIP, it can produce results with a higher quality than ours in terms of MSE, at the cost of a higher storage and per-material training. > Furthermore, our network can be applied to three typical types of woven fabrics once trained, but NeuMIP have to be trained per material which takes a long time > Specifically, we introduce a simple encoder-decoder structure, where the encoder compresses the fabric pattern and other parameters into a material latent vector, and the decoder interprets the material latent vector with a spatial fusion component and a small angular decoder. Thanks to the lightweight encoder and decoder, our network is able to achieve real-time rendering and editing. Meanwhile, our network only occupies a small storage of 5 MB, even for scenes with multiple fabric materials. Yeah. This is really just a simple solution that can be quickly trained and that compresses the problem space better at the cost of some fidelity.