  We present a learning-based system for rapid mass-scale material synthesis
that is useful for novice and expert users alike. The user preferences are
learned via Gaussian Process Regression and can be easily sampled for new
recommendations. Typically, each recommendation takes 40-60 seconds to render
with global illumination, which makes this process impracticable for real-world
workflows. Our neural network eliminates this bottleneck by providing
high-quality image predictions in real time, after which it is possible to pick
the desired materials from a gallery and assign them to a scene in an intuitive
manner. Workflow timings against Disney's "principled" shader reveal that our
system scales well with the number of sought materials, thus empowering even
novice users to generate hundreds of high-quality material models without any
expertise in material modeling. Similarly, expert users experience a
significant decrease in the total modeling time when populating a scene with
materials. Furthermore, our proposed solution also offers controllable
recommendations and a novel latent space variant generation step to enable the
real-time fine-tuning of materials without requiring any domain expertise.
