Modelling and Visualisation of the Optical Properties of Cloth

Cloth and garment visualisations are widely used in fashion and interior design, entertaining, automotive and nautical industry and are indispensable elements of visual communication. Modern appearance models attempt to offer a complete solution for the visualisation of complex cloth properties. In the review part of the chapter, advanced methods that enable visualisation at micron resolution, methods used in three-dimensional (3D) visualisation workflow and methods used for research purposes are presented. Within the review, those methods offering a comprehensive approach and experiments on explicit clothes attributes that present specific optical phenomenon are analysed. The review of appearance models includes surface and image-based models, volumetric and explicit models. Each group is presented with the representative authors’ research group and the application and limitations of the methods. In the final part of the chapter, the visualisation of cloth specularity and porosity with an uneven surface is studied. The study and visualisation was performed using image data obtained with photography. The acquisition of structure information on a large scale namely enables the recording of structure irregularities that are very common on historical textiles, laces and also on artistic and experimental pieces of cloth. The contribution ends with the presentation of cloth visualised with the use of specular and alpha maps, which is the result of the image processing workflow

Rendering the Renaissance: A Methodology for Recreating Historical Fabrics and Fashions in Computer Graphics

Fabric and costume is an integral part of film media and increasingly so in computer graphics. There exists a growing interest in the creation of period films. To stay true to historical accuracy, creating believable, accurate costumes with appropriate fabric is key. While films such as Pixar’s Brave have made attempts at visual accuracy, there is little existing literature discussing a method of creating such costumes. This thesis aims to form a methodology and approach to historical costume using available technology, extant historical garments, period artist renderings and real world fabrics and sewing technique. To approach this problem, a focus time period and location was selected for review and recreation. Due to the amount of visual data available, mid 16th century Florence proved a desirable candidate. Existing software packages Maya, Marvelous Designer, Mental Ray and Renderman were used for modeling, simulation and rendering respectively in order to execute the final product. The end goal was to render a model of a Florentine dress with identifiable fabrics using the designed methodology. An additional goal was to demonstrate a variety of fabric shaders to illustrate fabrics found during this period such as wool, linen, silk and velvet. The resulting renders represented visual accuracy to the sources used. Applications for this methodology can include film, games, historical documentation and education

Importance Sampling BRDF Derivatives

We propose a set of techniques to efficiently importance sample the derivatives of several BRDF models. In differentiable rendering, BRDFs are replaced by their differential BRDF counterparts which are real-valued and can have negative values. This leads to a new source of variance arising from their change in sign. Real-valued functions cannot be perfectly importance sampled by a positive-valued PDF and the direct application of BRDF sampling leads to high variance. Previous attempts at antithetic sampling only addressed the derivative with the roughness parameter of isotropic microfacet BRDFs. Our work generalizes BRDF derivative sampling to anisotropic microfacet models, mixture BRDFs, Oren-Nayar, Hanrahan-Krueger, among other analytic BRDFs. Our method first decomposes the real-valued differential BRDF into a sum of single-signed functions, eliminating variance from a change in sign. Next, we importance sample each of the resulting single-signed functions separately. The first decomposition, positivization, partitions the real-valued function based on its sign, and is effective at variance reduction when applicable. However, it requires analytic knowledge of the roots of the differential BRDF, and for it to be analytically integrable too. Our key insight is that the single-signed functions can have overlapping support, which significantly broadens the ways we can decompose a real-valued function. Our product and mixture decompositions exploit this property, and they allow us to support several BRDF derivatives that positivization could not handle. For a wide variety of BRDF derivatives, our method significantly reduces the variance (up to 58x in some cases) at equal computation cost and enables better recovery of spatially varying textures through gradient-descent-based inverse rendering

BxDF material acquisition, representation, and rendering for VR and design

Photorealistic and physically-based rendering of real-world environments with high fidelity materials is important to a range of applications, including special effects, architectural modelling, cultural heritage, computer games, automotive design, and virtual reality (VR). Our perception of the world depends on lighting and surface material characteristics, which determine how the light is reflected, scattered, and absorbed. In order to reproduce appearance, we must therefore understand all the ways objects interact with light, and the acquisition and representation of materials has thus been an important part of computer graphics from early days. Nevertheless, no material model nor acquisition setup is without limitations in terms of the variety of materials represented, and different approaches vary widely in terms of compatibility and ease of use. In this course, we describe the state of the art in material appearance acquisition and modelling, ranging from mathematical BSDFs to data-driven capture and representation of anisotropic materials, and volumetric/thread models for patterned fabrics. We further address the problem of material appearance constancy across different rendering platforms. We present two case studies in architectural and interior design. The first study demonstrates Yulio, a new platform for the creation, delivery, and visualization of acquired material models and reverse engineered cloth models in immersive VR experiences. The second study shows an end-to-end process of capture and data-driven BSDF representation using the physically-based Radiance system for lighting simulation and rendering

Visual Prototyping of Cloth

Realistic visualization of cloth has many applications in computer graphics. An ongoing research problem is how to best represent and capture appearance models of cloth, especially when considering computer aided design of cloth. Previous methods can be used to produce highly realistic images, however, possibilities for cloth-editing are either restricted or require the measurement of large material databases to capture all variations of cloth samples. We propose a pipeline for designing the appearance of cloth directly based on those elements that can be changed within the production process. These are optical properties of fibers, geometrical properties of yarns and compositional elements such as weave patterns. We introduce a geometric yarn model, integrating state-of-the-art textile research. We further present an approach to reverse engineer cloth and estimate parameters for a procedural cloth model from single images. This includes the automatic estimation of yarn paths, yarn widths, their variation and a weave pattern. We demonstrate that we are able to match the appearance of original cloth samples in an input photograph for several examples. Parameters of our model are fully editable, enabling intuitive appearance design. Unfortunately, such explicit fiber-based models can only be used to render small cloth samples, due to large storage requirements. Recently, bidirectional texture functions (BTFs) have become popular for efficient photo-realistic rendering of materials. We present a rendering approach combining the strength of a procedural model of micro-geometry with the efficiency of BTFs. We propose a method for the computation of synthetic BTFs using Monte Carlo path tracing of micro-geometry. We observe that BTFs usually consist of many similar apparent bidirectional reflectance distribution functions (ABRDFs). By exploiting structural self-similarity, we can reduce rendering times by one order of magnitude. This is done in a process we call non-local image reconstruction, which has been inspired by non-local means filtering. Our results indicate that synthesizing BTFs is highly practical and may currently only take a few minutes for small BTFs. We finally propose a novel and general approach to physically accurate rendering of large cloth samples. By using a statistical volumetric model, approximating the distribution of yarn fibers, a prohibitively costly, explicit geometric representation is avoided. As a result, accurate rendering of even large pieces of fabrics becomes practical without sacrificing much generality compared to fiber-based techniques