Steve Marschner

Matching Real Fabrics with Micro-Appearance Models

Micro-appearance models explicitly model the interaction of light with microgeometry at the fiber scale to produce realistic appearance. To effectively match them to real fabrics, we introduce a new appearance matching framework to determine their parameters. Given a micro-appearance model and photographs of the fabric under many different lighting conditions, we optimize for parameters that best match the photographs using a method based on calculating derivatives during rendering. This highly applicable framework, we believe, is a useful research tool because it simplifies development and testing of new models. Using the framework, we systematically compare several types of micro-appearance models. We acquired computed microtomography (micro CT) scans of several fabrics, photographed the fabrics under many viewing/illumination conditions, and matched several appearance models to this data. We compare a new fiber-based light scattering model to the previously used microflake model. We also compare representing cloth microgeometry using volumes derived directly from the micro CT data to using explicit fibers reconstructed from the volumes. From our comparisons, we make the following conclusions: (1) given a fiber-based scattering model, volume- and fiber-based microgeometry representations are capable of very similar quality, and (2) using a fiber-specific scattering model is crucial to good results as it achieves considerably higher accuracy than prior work.

Microstructures to control elasticity in 3D printing

We propose a method for fabricating deformable objects with spatially varying elasticity using 3D printing. Using a single, relatively stiff printer material, our method designs an assembly of small-scale microstructures that have the effect of a softer material at the object scale, with properties depending on the microstructure used in each part of the object. We build on work in the area of metamaterials, using numerical optimization to design tiled microstructures with desired properties, but with the key difference that our method designs families of related structures that can be interpolated to smoothly vary the material properties over a wide range. To create an object with spatially varying elastic properties, we tile the objects interior with microstructures drawn from these families, generating a different microstructure for each cell using an efficient algorithm to select compatible structures for neighboring cells. We show results computed for both 2D and 3D objects, validating several 2D and 3D printed structures using standard material tests as well as demonstrating various example applications.

Manifold exploration: a Markov Chain Monte Carlo technique for rendering scenes with difficult specular transport

It is a long-standing problem in unbiased Monte Carlo methods for rendering that certain difficult types of light transport paths, particularly those involving viewing and illumination along paths containing specular or glossy surfaces, cause unusably slow convergence. In this paper we introduce Manifold Exploration, a new way of handling specular paths in rendering. It is based on the idea that sets of paths contributing to the image naturally form manifolds in path space, which can be explored locally by a simple equation-solving iteration. This paper shows how to formulate and solve the required equations using only geometric information that is already generally available in ray tracing systems, and how to use this method in in two different Markov Chain Monte Carlo frameworks to accurately compute illumination from general families of paths. The resulting rendering algorithms handle specular, near-specular, glossy, and diffuse surface interactions as well as isotropic or highly anisotropic volume scattering interactions, all using the same fundamental algorithm. An implementation is demonstrated on a range of challenging scenes and evaluated against previous methods.

Building volumetric appearance models of fabric using micro CT imaging

The appearance of complex, thick materials like textiles is determined by their 3D structure, and they are incompletely described by surface reflection models alone. While volume scattering can produce highly realistic images of such materials, creating the required volume density models is difficult. Procedural approaches require significant programmer effort and intuition to design specialpurpose algorithms for each material. Further, the resulting models lack the visual complexity of real materials with their naturally-arising irregularities. This paper proposes a new approach to acquiring volume models, based on density data from X-ray computed tomography (CT) scans and appearance data from photographs under uncontrolled illumination. To model a material, a CT scan is made, resulting in a scalar density volume. This 3D data is processed to extract orientation information and remove noise. The resulting density and orientation fields are used in an appearance matching procedure to define scattering properties in the volume that, when rendered, produce images with texture statistics that match the photographs. As our results show, this approach can easily produce volume appearance models with extreme detail, and at larger scales the distinctive textures and highlights of a range of very different fabrics like satin and velvet emerge automatically---all based simply on having accurate mesoscale geometry.

Physical face cloning

We propose a complete process for designing, simulating, and fabricating synthetic skin for an animatronics character that mimics the face of a given subject and its expressions. The process starts with measuring the elastic properties of a material used to manufacture synthetic soft tissue. Given these measurements we use physics-based simulation to predict the behavior of a face when it is driven by the underlying robotic actuation. Next, we capture 3D facial expressions for a given target subject. As the key component of our process, we present a novel optimization scheme that determines the shape of the synthetic skin as well as the actuation parameters that provide the best match to the target expressions. We demonstrate this computational skin design by physically cloning a real human face onto an animatronics figure.

Efficient yarn-based cloth with adaptive contact linearization

Yarn-based cloth simulation can improve visual quality but at high computational costs due to the reliance on numerous persistent yarn-yarn contacts to generate material behavior. Finding so many contacts in densely interlinked geometry is a pathological case for traditional collision detection, and the sheer number of contact interactions makes contact processing the simulation bottleneck. In this paper, we propose a method for approximating penalty-based contact forces in yarn-yarn collisions by computing the exact contact response at one time step, then using a rotated linear force model to approximate forces in nearby deformed configurations. Because contacts internal to the cloth exhibit good temporal coherence, sufficient accuracy can be obtained with infrequent updates to the approximation, which are done adaptively in space and time. Furthermore, by tracking contact models we reduce the time to detect new contacts. The end result is a 7- to 9-fold speedup in contact processing and a 4- to 5-fold overall speedup, enabling simulation of character-scale garments.

Data-Driven Estimation of Cloth Simulation Models

Progress in cloth simulation for computer animation and apparel design has led to a multitude of deformation models, each with its own way of relating geometry, deformation, and forces. As simulators improve, differences between these models become more important, but it is difficult to choose a model and a set of parameters to match a given real material simply by looking at simulation results. This paper provides measurement and fitting methods that allow nonlinear models to be fit to the observed deformation of a particular cloth sample. Unlike standard textile testing, our system measures complex 3D deformations of a sheet of cloth, not just one‐dimensional force‐displacement curves, so it works under a wider range of deformation conditions. The fitted models are then evaluated by comparison to measured deformations with motions very different from those used for fitting.

Coupled 3D reconstruction of sparse facial hair and skin

Although facial hair plays an important role in individual expression, facial-hair reconstruction is not addressed by current face-capture systems. Our research addresses this limitation with an algorithm that treats hair and skin surface capture together in a coupled fashion so that a high-quality representation of hair fibers as well as the underlying skin surface can be reconstructed. We propose a passive, camera-based system that is robust against arbitrary motion since all data is acquired within the time period of a single exposure. Our reconstruction algorithm detects and traces hairs in the captured images and reconstructs them in 3D using a multiview stereo approach. Our coupled skin-reconstruction algorithm uses information about the detected hairs to deliver a skin surface that lies underneath all hairs irrespective of occlusions. In dense regions like eyebrows, we employ a hair-synthesis method to create hair fibers that plausibly match the image data. We demonstrate our scanning system on a number of individuals and show that it can successfully reconstruct a variety of facial-hair styles together with the underlying skin surface.

Capturing hair assemblies fiber by fiber

Hair models for computer graphics consist of many curves representing individual hair fibers. In current practice these curves are generated by ad hoc random processes, and in close-up views their arrangement appears plainly different from real hair. To begin improving this situation, this paper presents a new method for measuring the detailed arrangement of fibers in a hair assembly. Many macrophotographs with shallow depth of field are taken of a sample of hair, sweeping the plane of focus through the hairs volume. The shallow depth of field helps isolate the fibers and reduces occlusion. Several sweeps are performed with the hair at different orientations, resulting in multiple observations of most of the clearly visible fibers. The images are filtered to detect the fibers, and the resulting feature data from all images is used jointly in a hair growing process to construct smooth curves along the observed fibers. Finally, additional hairs are generated to fill in the unseen volume inside the hair. The method is demonstrated on both straight and wavy hair, with results suitable for realistic close-up renderings. These models provide the first views we know of into the 3D arrangement of hair fibers in real hair assemblies.

Efficient multiple scattering in hair using spherical harmonics

Previous research has shown that a global multiple scattering simulation is needed to achieve physically realistic renderings of hair, particularly light-colored hair with low absorption. However, previous methods have either sacrificed accuracy or have been too computationally expensive for practical use. In this paper we describe a physically based, volumetric rendering method that computes multiple scattering solutions, including directional effects, much faster than previous accurate methods. Our two-pass method first traces light paths through a volumetric representation of the hair, contributing power to a 3D grid of spherical harmonic coefficients that store the directional distribution of scattered radiance everywhere in the hair volume. Then, in a ray tracing pass, multiple scattering is computed by integrating the stored radiance against the scattering functions of visible fibers using an efficient matrix multiplication. Single scattering is computed using conventional direct illumination methods. In our comparisons the new method produces quality similar to that of the best previous methods, but computes multiple scattering more than 10 times faster.