  We propose NeuMIP, a neural method for representing and rendering a variety
of material appearances at different scales. Classical prefiltering
(mipmapping) methods work well on simple material properties such as diffuse
color, but fail to generalize to normals, self-shadowing, fibers or more
complex microstructures and reflectances. In this work, we generalize
traditional mipmap pyramids to pyramids of neural textures, combined with a
fully connected network. We also introduce neural offsets, a novel method which
allows rendering materials with intricate parallax effects without any
tessellation. This generalizes classical parallax mapping, but is trained
without supervision by any explicit heightfield. Neural materials within our
system support a 7-dimensional query, including position, incoming and outgoing
direction, and the desired filter kernel size. The materials have small storage
(on the order of standard mipmapping except with more texture channels), and
can be integrated within common Monte-Carlo path tracing systems. We
demonstrate our method on a variety of materials, resulting in complex
appearance across levels of detail, with accurate parallax, self-shadowing, and
other effects.
