Facial appearance depends on both the physical and physiological state of the skin. As people move, talk, undergo stress, and change expression, skin appearance is in constant flux. One of the key indicators of these changes is thecolorof skin. Skin color is determined by scattering and absorption of light within the skin layers, caused mostly by concentrations of two chromophores, melanin and hemoglobin. In this paper we present a real-time dynamic appearance model of skin built fromin vivomeasurements of melanin and hemoglobin concentrations. We demonstrate an efficient implementation of our method, and show that it adds negligible overhead to existing animation and rendering pipelines. Additionally, we develop a realistic, intuitive, and automatic control for skin color, which we term askin appearance rig.This rig can easily be coupled with a traditional geometric facial animation rig. We demonstrate our method by augmenting digital facial performance with realistic appearance changes.

We present a new model of the homogeneous BSSRDF based on large-scale simulations. Our model captures the appearance of materials that are not accurately represented using existing single scattering models or multiple isotropic scattering models (e.g. the diffusion approximation). We use an analytic function to model the 2D hemispherical distribution of exitant light at a point on the surface, and a table of parameter values of this function computed at uniformly sampled locations over the remaining dimensions of the BSSRDF domain. This analytic function is expressed in elliptic coordinates and has six parameters which vary smoothly with surface position, incident angle, and the underlying optical properties of the material (albedo, mean free path length, phase function and the relative index of refraction). Our model agrees well with measured data, and is compact, requiring only 250MB to represent the full spatial- and angular-distribution of light across a wide spectrum of materials. In practice, rendering a single material requires only about 100KB to represent the BSSRDF.

Effects such as depth of field, area lighting, antialiasing and global illumination require evaluating a complex high-dimensional integral at each pixel of an image. We develop a new adaptive rendering algorithm that greatly reduces the number of samples needed for Monte Carlo integration. Our method renders directly into an image-space wavelet basis. First, we adaptively distribute Monte Carlo samples to reduce the variance of the wavelet basis' scale coefficients, while using the wavelet coefficients to find edges. Working in wavelets, rather than pixels, allows us to sample not only image-space edges but also other features that are smooth in the image plane but have high variance in other integral dimensions. In the second stage, we reconstruct the image from these samples by using a suitable wavelet approximation. We achieve this by subtracting an estimate of the error in each wavelet coefficient from its magnitude, effectively producing the smoothest image consistent with the rendering samples. Our algorithm renders scenes with significantly fewer samples than basic Monte Carlo or adaptive techniques. Moreover, the method introduces minimal overhead, and can be efficiently included in an optimized ray-tracing system.

We introduce a layered, heterogeneous spectral reflectance model for human skin. The model captures the inter-scattering of light among layers, each of which may have an independent set of spatially-varying absorption and scattering parameters. For greater physical accuracy and control, we introduce an infinitesimally thin absorbing layer between scattering layers. To obtain parameters for our model, we use a novel acquisition method that begins with multi-spectral photographs. By using an inverse rendering technique, along with known chromophore spectra, we optimize for the best set of parameters for each pixel of a patch. Our method finds close matches to a wide variety of inputs with low residual error. We apply our model to faithfully reproduce the complex variations in skin pigmentation. This is in contrast to most previous work, which assumes that skin is homogeneous or composed of homogeneous layers. We demonstrate the accuracy and flexibility of our model by creating complex skin visual effects such as veins, tattoos, rashes, and freckles, which would be difficult to author using only albedo textures at the skin's outer surface. Also, by varying the parameters to our model, we simulate effects from external forces, such as visible changes in blood flow within the skin due to external pressure.

In this article we present a novel radiance caching method for efficiently rendering participating media using Monte Carlo ray tracing. Our method handles all types of light scattering including anisotropic scattering, and it works in both homogeneous and heterogeneous media. A key contribution in the article is a technique for computing gradients of radiance evaluated in participating media. These gradients take the full path of the scattered light into account including the changing properties of the medium in the case of heterogeneous media. The gradients can be computed simultaneously with the inscattered radiance with negligible overhead. We compute gradients for single scattering from lights and surfaces and for multiple scattering, and we use a spherical harmonics representation in media with anisotropic scattering. Our second contribution is a new radiance caching scheme for participating media. This caching scheme uses the information in the radiance gradients to sparsely sample as well as interpolate radiance within the medium utilizing a novel, perceptually based error metric. Our method provides several orders of magnitude speedup compared to path tracing and produces higher quality results than volumetric photon mapping. Furthermore, it is view-driven and well suited for large scenes where methods such as photon mapping become costly.

The visual world around us displays a rich set of volumetric effects due to participating media. The appearance of these media is governed by several physical properties such as particle densities, shapes and sizes, which must be input (directly or indirectly) to a rendering algorithm to generate realistic images. While there has been significant progress in developing rendering techniques (for instance, volumetric Monte Carlo methods and analytic approximations), there are very few methods that measure or estimate these properties for media that are of relevance to computer graphics. In this paper, we present a simple device and technique for robustly estimating the properties of a broad class of participating media that can be either (a) diluted in water such as juices, beverages, paints and cleaning supplies, or (b) dissolved in water such as powders and sugar/salt crystals, or (c) suspended in water such as impurities. The key idea is to dilute the concentrations of the media so that single scattering effects dominate and multiple scattering becomes negligible, leading to a simple and robust estimation algorithm. Furthermore, unlike previous approaches that require complicated or separate measurement setups for different types or properties of media, our method and setup can be used to measure media with a complete range of absorption and scattering properties from a single HDR photograph. Once the parameters of the diluted medium are estimated, a volumetric Monte Carlo technique may be used to create renderings of any medium concentration and with multiple scattering. We have measured the scattering parameters of forty commonly found materials, that can be immediately used by the computer graphics community. We can also create realistic images of combinations or mixtures of the original measured materials, thus giving the user a wide flexibility in making realistic images of participating media.

We have measured 3D face geometry, skin reflectance, and subsurface scattering using custom-built devices for 149 subjects of varying age, gender, and race. We developed a novel skin reflectance model whose parameters can be estimated from measurements. The model decomposes the large amount of measured skin data into a spatially-varying analytic BRDF, a diffuse albedo map, and diffuse subsurface scattering. Our model is intuitive, physically plausible, and -- since we do not use the original measured data -- easy to edit as well. High-quality renderings come close to reproducing real photographs. The analysis of the model parameters for our sample population reveals variations according to subject age, gender, skin type, and external factors (e.g., sweat, cold, or makeup). Using our statistics, a user can edit the overall appearance of a face (e.g., changing skin type and age) or change small-scale features using texture synthesis (e.g., adding moles and freckles). We are making the collected statistics publicly available to the research community for applications in face synthesis and analysis.

This paper introduces a shading model for light diffusion in multi-layered translucent materials. Previous work on diffusion in translucent materials has assumed smooth semi-infinite homogeneous materials and solved for the scattering of light using a dipole diffusion approximation. This approximation breaks down in the case of thin translucent slabs and multi-layered materials. We present a new efficient technique based on multiple dipoles to account for diffusion in thin slabs. We enhance this multipole theory to account for mismatching indices of refraction at the top and bottom of of translucent slabs, and to model the effects of rough surfaces. To model multiple layers, we extend this single slab theory by convolving the diffusion profiles of the individual slabs. We account for multiple scattering between slabs by using a variant of Kubelka-Munk theory in frequency space. Our results demonstrate diffusion of light in thin slabs and multi-layered materials such as paint, paper, and human skin.