  We learn a latent space for easy capture, semantic editing, consistent
interpolation, and efficient reproduction of visual material appearance. When
users provide a photo of a stationary natural material captured under flash
light illumination, it is converted in milliseconds into a latent material
code. In a second step, conditioned on the material code, our method, again in
milliseconds, produces an infinite and diverse spatial field of BRDF model
parameters (diffuse albedo, specular albedo, roughness, normals) that allows
rendering in complex scenes and illuminations, matching the appearance of the
input picture. Technically, we jointly embed all flash images into a latent
space using a convolutional encoder, and -- conditioned on these latent codes
-- convert random spatial fields 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 confirms that the semantics of the
latent material space agree with user expectations and compares our approach
favorably to previous work.
