Timonen, HeikkiKemppinen, PauliLehtinen, JaakkoMasia, BelenThies, Justus2026-04-172026-04-1720261467-8659https://diglib.eg.org/handle/10.1111/cgf70331https://doi.org/10.1111/cgf.70331Recent machine learning methods have significantly advanced the state of the art in the classic problem of representing surface appearance over angle, space, and scale. The models tend, however, to be relatively heavy compared to traditional fixedfunction representations, making real-time application challenging. We present a neural shading architecture that allows the use of smaller and faster-to-evaluate neural networks than current state of the art, while faithfully representing complex spatial and angular variation. We target the angular complexity that arises both from prefiltering normal-mapped SVBRDFs, as well as complex, measured homogeneous BRDFs. A key architectural innovation is the introduction of a multiplicative interaction ("gating") between learnable parameters that significantly increases our model's expressive power. Our straightforward, unoptimized shader implementation renders over 1000 full HD frames per second on a consumer GPU using our default parameters.CC-BY-4.0Computing methodologies → Reflectance modelingNeural networks10.1111/cgf.7033114 pages