  Photo realism in computer generated imagery is crucially dependent on how
well an artist is able to recreate real-world materials in the scene. The
workflow for material modeling and editing typically involves manual tweaking
of material parameters and uses a standard path tracing engine for visual
feedback. A lot of time may be spent in iterative selection and rendering of
materials at an appropriate quality. In this work, we propose a convolutional
neural network based workflow which quickly generates high-quality ray traced
material visualizations on a shaderball. Our novel architecture allows for
control over environment lighting and assists material selection along with the
ability to render spatially-varying materials. Additionally, our network
enables control over environment lighting which gives an artist more freedom
and provides better visualization of the rendered material. Comparison with
state-of-the-art denoising and neural rendering techniques suggests that our
neural renderer performs faster and better. We provide a interactive
visualization tool and release our training dataset to foster further research
in this area.
