In this work, we introduce an extension to microfacet theory for the rendering of iridescent effects caused by thin-films of varying thickness (such as oil, grease, alcohols, etc) on top of an arbitrarily rough base layer. Our material model is the first to produce a consistent appearance between tristimulus (e.g., RGB) and spectral rendering engines by analytically pre-integrating its spectral response. The proposed extension works with any microfacet-based model: not only on reflection over dielectrics or conductors, but also on transmission through dielectrics. We adapt its evaluation to work in multi-scale rendering contexts, and we expose parameters enabling artistic control over iridescent appearance. The overhead compared to using the classic Fresnel reflectance or transmittance terms remains reasonable enough for practical uses in production.

SummaryPerceptual constancy—identifying surfaces and objects across large image changes—remains an important challenge for visual neuroscience [1, 2, 3, 4, 5, 6, 7, 8]. Liquids are particularly challenging because they respond to external forces in complex, highly variable ways, presenting an enormous range of images to the visual system. To achieve constancy, the brain must perform a causal inference [9, 10, 11] that disentangles the liquid’s viscosity from external factors—like gravity and object interactions—that also affect the liquid’s behavior. Here, we tested whether the visual system estimates viscosity using “midlevel” features [12, 13, 14] that respond more to viscosity than other factors. Observers reported the perceived viscosity of simulated liquids ranging from water to molten glass exhibiting diverse behaviors (e.g., pouring, stirring). A separate group of observers rated the same animations for 20 midlevel 3D shape and motion features. Applying factor analysis to the feature ratings reveals that a weighted combination of four underlying factors (distribution, irregularity, rectilinearity, and dynamics) predicted perceived viscosity very well across this wide range of contexts (R2 = 0.93). Interestingly, observers unknowingly ordered their midlevel judgments according to the one common factor across contexts: variation in viscosity. Principal component analysis reveals that across the features, the first component lines up almost perfectly with the viscosity (R2 = 0.96). Our findings demonstrate that the visual system achieves constancy by representing stimuli in a multidimensional feature space—based on complementary, midlevel features—which successfully cluster very different stimuli together and tease similar stimuli apart, so that viscosity can be read out easily.

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Figure 1: Our novel Radiance Scaling technique enhances the depiction of surface shape under arbitrary illumination, with various materials, and in a wide range of rendering settings. In the left pair of images, we illustrate how surface features are enhanced mainly through enhancement of the specular shading term. Whereas on the right pair of images, we show the efficiency of our method on an approximation of a refractive material. Observe how various surface details are enhanced in both cases: around the eyes, inside the ear, and on the nose.