  Our objective is to compute a textural loss that can be used to train texture
generators with multiple material channels typically used for physically based
rendering such as albedo, normal, roughness, metalness, ambient occlusion, etc.
Neural textural losses often build on top of the feature spaces of pretrained
convolutional neural networks. Unfortunately, these pretrained models are only
available for 3-channel RGB data and hence limit neural textural losses to this
format. To overcome this limitation, we show that passing random triplets to a
3-channel loss provides a multi-channel loss that can be used to generate
high-quality material textures.
