  We address the problem of reconstructing spatially-varying BRDFs from a small
set of image measurements. This is a fundamentally under-constrained problem,
and previous work has relied on using various regularization priors or on
capturing many images to produce plausible results. In this work, we present
MaterialGAN, a deep generative convolutional network based on StyleGAN2,
trained to synthesize realistic SVBRDF parameter maps. We show that MaterialGAN
can be used as a powerful material prior in an inverse rendering framework: we
optimize in its latent representation to generate material maps that match the
appearance of the captured images when rendered. We demonstrate this framework
on the task of reconstructing SVBRDFs from images captured under flash
illumination using a hand-held mobile phone. Our method succeeds in producing
plausible material maps that accurately reproduce the target images, and
outperforms previous state-of-the-art material capture methods in evaluations
on both synthetic and real data. Furthermore, our GAN-based latent space allows
for high-level semantic material editing operations such as generating material
variations and material morphing.
