An intuitive control space for material appearance

TL;DR

This paper introduces an intuitive control space for editing captured BRDF data, enabling artistic creation of new materials and providing tools for perceptual analysis and editing of material appearance.

Contribution

It extends the MERL dataset with 400 novel BRDFs, develops perceptual attribute-based functionals, and offers a new approach for predictable, artistic material editing.

Findings

High correlation between perceptual attributes and BRDF representations

Effective prediction of perceived appearance using trained functionals

Enabling intuitive editing and analysis of material appearance

Abstract

Many different techniques for measuring material appearance have been proposed in the last few years. These have produced large public datasets, which have been used for accurate, data-driven appearance modeling. However, although these datasets have allowed us to reach an unprecedented level of realism in visual appearance, editing the captured data remains a challenge. In this paper, we present an intuitive control space for predictable editing of captured BRDF data, which allows for artistic creation of plausible novel material appearances, bypassing the difficulty of acquiring novel samples. We first synthesize novel materials, extending the existing MERL dataset up to 400 mathematically valid BRDFs. We then design a large-scale experiment, gathering 56,000 subjective ratings on the high-level perceptual attributes that best describe our extended dataset of materials. Using these ratings, we build and train networks of radial basis functions to act as functionals mapping the perceptual attributes to an underlying PCA-based representation of BRDFs. We show that our functionals are excellent predictors of the perceived attributes of appearance. Our control space enables many applications, including intuitive material editing of a wide range of visual properties, guidance for gamut mapping, analysis of the correlation between perceptual attributes, or novel appearance similarity metrics. Moreover, our methodology can be used to derive functionals applicable to classic analytic BRDF representations. We release our code and dataset publicly, in order to support and encourage further research in this direction.