Despite the notable progress in physically-based rendering, there is still a long way to go before we can automatically generate predictable images of biological materials. In this paper, we address an open problem in this area, namely the spectral simulation of light interaction with human skin. We propose a novel biophysicallybased model that accounts for all components of light propagation in skin tissues, namely surface reflectance, subsurface reflectance and transmittance, and the biological mechanisms of light absorption by pigments in these tissues. The model is controlled by biologically meaningful parameters, and its formulation, based on standard Monte Carlo techniques, enables its straightforward incorporation into realistic image synthesis frameworks. Besides its biophysically-based nature, the key difference between the proposed model and the existing skin models is its comprehensiveness, i.e., it computes both spectral (reflectance and transmittance) and scattering (bidirectional surface-scattering distribution function) quantities for skin specimens. In order to assess the predictability of our simulations, we evaluate their accuracy by comparing results from the model with actual skin measured data. We also present computer generated images to illustrate the flexibility of the proposed model with respect to variations in the biological input data, and its applicability not only in the predictive image synthesis of different skin tones, but also in the spectral simulation of medical conditions.

We introduce a physiologically-based model for pupil light reflex (PLR) and an image-based model for iridal pattern deformation. Our PLR model expresses the pupil diameter as a function of the lighting of the environment, and is described by a delay-differential equation, naturally adapting the pupil diameter even to abrupt changes in light conditions. Since the parameters of our PLR model were derived from measured data, it correctly simulates the actual behavior of the human pupil. Another contribution of our work is a model for realistic deformation of the iris pattern as a function of pupil dilation and constriction. Our models produce high-fidelity appearance effects and can be used to produce real-time predictive animations of the pupil and iris under variable lighting conditions. We assess the predictability and quality of our simulations through comparisons of modeled results against measured data derived from experiments also described in this work. Combined, our models can bring facial animation to new photorealistic standards.

Exploration of the hyperspectral domain offers a host of new research and application possibilities involving material appearance modeling. In this article, we address these prospects with respect to human skin, one of the most ubiquitous materials portrayed in synthetic imaging. We present the first hyperspectral model designed for the predictive rendering of skin appearance attributes in the ultraviolet, visible, and infrared domains. The proposed model incorporates the intrinsic bio-optical properties of human skin affecting light transport in these spectral regions, including the particle nature and distribution patterns of the main light attenuation agents found within the cutaneous tissues. Accordingly, it accounts for phenomena that significantly affect skin spectral signatures, both within and outside the visible domain, such as detour and sieve effects, that are overlooked by existing skin appearance models. Using a first-principles approach, the proposed model computes the surface and subsurface scattering components of skin reflectance taking into account not only the wavelength and the illumination geometry, but also the positional dependence of the reflected light. Hence, the spectral and spatial distributions of light interacting with human skin can be comprehensively represented in terms of hyperspectral reflectance and BSSRDF, respectively.

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In this paper, we present a new spectral light transport model for sand. The model employs a novel approach to simulate light interaction with particulate materials which yields both the spectral and spatial (bi-directional reflectance distribution function, or BRDF) responses of sand. Furthermore, the parameters specifying the model are based on the physical and mineralogical properties of sand. The model is evaluated quantitatively, through comparisons with measured data. Good spectral reconstructions were achieved for the reflectances of several real sand samples. The model was also evaluated qualitatively, and compares well with descriptions found in the literature. Its potential applications include, but are not limited to, applied optics, remote sensing and image synthesis.