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We learn a latent space for easy capture, consistent interpolation, andefficient reproduction of visual material appearance. When users provide aphoto of a stationary natural material captured under flashlight illumination,first it is converted into a latent material code. Then, in the second step,conditioned on the material code, our method produces an infinite and diversespatial field of BRDF model parameters (diffuse albedo, normals, roughness,specular albedo) that subsequently allows rendering in complex scenes andilluminations, matching the appearance of the input photograph. Technically, wejointly embed all flash images into a latent space using a convolutionalencoder, and -- conditioned on these latent codes -- convert random spatialfields into fields of BRDF parameters using a convolutional neural network(CNN). We condition these BRDF parameters to match the visual characteristics(statistics and spectra of visual features) of the input under matching light.A user study compares our approach favorably to previous work, even those withaccess to BRDF supervision.

Generative Modelling of BRDF Textures from Flash Images