  We propose a material appearance modeling neural network for visualizing
plausible, spatially-varying materials under diverse view and lighting
conditions, utilizing only a single photograph of a material under co-located
light and view as input for appearance estimation. Our neural architecture is
composed of two network stages: a network that infers learned per-pixel neural
parameters of a material from a single input photograph, and a network that
renders the material utilizing these neural parameters, similar to a BRDF. We
train our model on a set of 312,165 synthetic spatially-varying exemplars.
Since our method infers learned neural parameters rather than analytical BRDF
parameters, our method is capable of encoding anisotropic and global
illumination (inter-pixel interaction) information into individual pixel
parameters. We demonstrate our model's performance compared to prior work and
demonstrate the feasibility of the render network as a BRDF by implementing it
into the Mitsuba3 rendering engine. Finally, we briefly discuss the capability
of neural parameters to encode global illumination information.
