  We propose a learning-based method to recover normals, specularity, and
roughness from a single diffuse image of a material, using microgeometry
appearance as our primary cue. Previous methods that work on single images tend
to produce over-smooth outputs with artifacts, operate at limited resolution,
or train one model per class with little room for generalization. Previous
methods that work on single images tend to produce over-smooth outputs with
artifacts, operate at limited resolution, or train one model per class with
little room for generalization. In contrast, in this work, we propose a novel
capture approach that leverages a generative network with attention and a U-Net
discriminator, which shows outstanding performance integrating global
information at reduced computational complexity. We showcase the performance of
our method with a real dataset of digitized textile materials and show that a
commodity flatbed scanner can produce the type of diffuse illumination required
as input to our method. Additionally, because the problem might be illposed --
more than a single diffuse image might be needed to disambiguate the specular
reflection -- or because the training dataset is not representative enough of
the real distribution, we propose a novel framework to quantify the model's
confidence about its prediction at test time. Our method is the first one to
deal with the problem of modeling uncertainty in material digitization,
increasing the trustworthiness of the process and enabling more intelligent
strategies for dataset creation, as we demonstrate with an active learning
experiment.

Computer Vision and Pattern Recognition;Artificial Intelligence;Graphics;Machine Learning