A Multi-scale Yarn Appearance Model with Fiber Details

Rendering realistic cloth has always been a challenge due to its intricate structure. Cloth is made up of fibers, plies, and yarns, and previous curved-based models, while detailed, were computationally expensive and inflexible for large cloth. To address this, we propose a simplified approach. We introduce a geometric aggregation technique that reduces ray-tracing computation by using fewer curves, focusing only on yarn curves. Our model generates ply and fiber shapes implicitly, compensating for the lack of explicit geometry with a novel shadowing component. We also present a shading model that simplifies light interactions among fibers by categorizing them into four components, accurately capturing specular and scattered light in both forward and backward directions. To render large cloth efficiently, we propose a multi-scale solution based on pixel coverage. Our yarn shading model outperforms previous methods, achieving rendering speeds 3-5 times faster with less memory in near-field views. Additionally, our multi-scale solution offers a 20% speed boost for distant cloth observation

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

A Multi-scale Yarn Appearance Model with Fiber Details

Rendering realistic cloth has always been a challenge due to its intricate structure. Cloth is made up of fibers, plies, and yarns, and previous curved-based models, while detailed, were computationally expensive and inflexible for large cloth. To address this, we propose a simplified approach. We introduce a geometric aggregation technique that reduces ray-tracing computation by using fewer curves, focusing only on yarn curves. Our model generates ply and fiber shapes implicitly, compensating for the lack of explicit geometry with a novel shadowing component. We also present a shading model that simplifies light interactions among fibers by categorizing them into four components, accurately capturing specular and scattered light in both forward and backward directions. To render large cloth efficiently, we propose a multi-scale solution based on pixel coverage. Our yarn shading model outperforms previous methods, achieving rendering speeds 3-5 times faster with less memory in near-field views. Additionally, our multi-scale solution offers a 20% speed boost for distant cloth observation

Woven fabric model creation from a single image

We present a fast, novel image-based technique for reverse engineering woven fabrics at a yarn level. These models can be used in a wide range of interior design and visual special effects applications. To recover our pseudo-Bidirectional Texture Function (BTF), we estimate the three-dimensional (3D) structure and a set of yarn parameters (e.g., yarnwidth, yarn crossovers) from spatial and frequency domain cues. Drawing inspiration from previous work [Zhao et al. 2012], we solve for the woven fabric pattern and from this build a dataset. In contrast, however, we use a combination of image space analysis and frequency domain analysis, and, in challenging cases, match image statistics with those from previously captured known patterns. Our method determines, from a single digital image, captured with a digital single-lens reflex (DSLR) camera under controlled uniform lighting, thewoven cloth structure, depth, and albedo, thus removing the need for separately measured depth data. The focus of this work is on the rapid acquisition of woven cloth structure and therefore we use standard approaches to render the results. Our pipeline first estimates the weave pattern, yarn characteristics, and noise statistics using a novel combination of low-level image processing and Fourier analysis. Next, we estimate a 3D structure for the fabric sample using a first-order Markov chain and our estimated noise model as input, also deriving a depth map and an albedo. Our volumetric textile model includes information about the 3D path of the center of the yarns, their variable width, and hence the volume occupied by the yarns, and colors. We demonstrate the efficacy of our approach through comparison images of test scenes rendered using (a) the original photograph, (b) the segmented image, (c) the estimated weave pattern, and (d) the rendered result

가상 의복의 생성, 수정 및 시뮬레이션을 위한 조작이 간편하고 문제를 발생시키지 않는 방법에 대한 연구

학위논문 (박사)-- 서울대학교 대학원 : 자연과학대학 협동과정 계산과학전공, 2016. 2. 고형석.This dissertation presents new methods for the construction, editing, and simulation of virtual garments. First, we describe a construction method called TAGCON, which constructs three-dimensional (3D) virtual garments from the given tagged and packed panels. Tagging and packing are performed by the user, and involve simple labeling and two-dimensional (2D) manipulation of the panelshowever, it does not involve any 3D manipulation. Then, TAGCON constructs the garment automatically by using algorithms that (1) position the panels at suitable locations around the body, and (2) find the matching seam lines and create the seam. We perform experiments using TAGCON to construct various types of garments. The proposed method significantly reduces the construction time and cumbersomeness. Secondly, we propose a method to edit virtual garments with synced 2D and 3D modification. The presented methods of linear interpolation, extrapolation, and penetration detection help users to edit the virtual garment interactively without the loss of 2D and 3D synchronization. After that, we propose a method to model the non-elastic components in the fabric stretch deformation in the context of developing physically based fabric simulator. We find that the above problem can be made tractable if we decompose the stretch deformation into the immediate elastic, viscoelastic, and plastic components. For the purpose of the simulator development, the decomposition must be possible at any stage of deformation and any occurrence of loading and unloading. Based on the observations of various constant force creep measurements, we make an assumption that, within a particular fabric, the viscoelastic and plastic components are proportional to each other and their ratio is invariant over time. Experimental results produced with the proposed method match with general expectations, and show that the method can represent the non-elastic stretch deformation for arbitrary time-varying force. In addition, we present a method to represent stylish elements of garments such as pleats and lapels. Experimental results show that the proposed method is effective at resolving problems that are not easily resolved using physically based cloth simulators.Chapter 1 Introduction 1 1.1 Digital Clothing 1 1.2 Garment Modeling 5 1.3 Physical Cloth Simulation 7 1.4 Dissertation Overview 9 Chapter 2 Previous Work 11 2.1 Garment Modeling 11 2.2 Physical Cloth Simulation 15 Chapter 3 Automatic Garment Construction from Pattern Analysis 17 3.1 Panel Classification 19 3.1.1 Panel Tagging 19 3.1.2 Panel Packing 22 3.1.3 Tagging-and-Packing Process 23 3.2 Classification of Seam-Line 24 3.3 Seam Creation 25 3.3.1 Creating the Intra-pack Seams 26 3.3.2 Creating the Inter-pack Seams 27 3.3.3 Creating the Inter-layer Seams 30 3.3.4 Seam-creation Process 31 3.4 Experiments 32 3.5 Conclusion 34 Chapter 4 Synced Garment Editing 39 4.1 Introduction to Synced Garment Editing 39 4.2 Geometric Approaches vs. Sensitivity Analysis 41 4.3 Trouble Free Synced Garment Editing 43 Chapter 5 Physically Based Non-Elastic Clothing Simulation 49 5.1 Classification of Deformation 50 5.2 Modeling Non-Elastic Deformations 53 5.2.1 Development of the Non-Elastic Model 55 5.2.2 Parameter Value Determination 60 5.3 Implementation 61 5.4 Experiments 65 Chapter 6 Tangle Avoidance with Pre-Folding 73 6.1 Problem of the First Frame Tangle 73 6.2 Tangle Avoidance with Pre-Folding 75 Chapter 7 Conclusion 81 Appendix A Simplification in the Decomposition of Stretch Deformation 85 Bibliography 87 초 록 99Docto

Spatially- And Directionally-Varying Reflectance Of Milli-Scale Feather Morphology

Birds have evolved diverse plumage through sophisticated morphological modifications. The interaction of light with these modifications alters the reflectance from feathers, producing complex and directionally-variable visual signals. I hypothesize that structural modifications of the feather produce anisotropic reflectance, the direction of which is determined by the orientation of the structure of the vane. Variation in reflectance originates from the interplay of light with two classes of feather structure: its surface and subsurface volume. Different structural scales within the two structural classes influence light scattering within the UV-visible spectrum. The overall shape and surface of the feather vane (the macro-scale) and of its component members (the milli-scale) scatter light according to principles of geometric optics. Subsurface nano-scale structure in many feathers generate socalled "structural coloration," which is a purely physical optics phenomenon and can differ drastically from ordinary coloration mechanisms such as pigmentation. Iridescence, from which many feathers derive their vivid, eye-catching changeable color, is one type of structural color that varies as a function of viewing angle. This thesis presents investigations into a previously understudied aspect of avian visual signaling: directional reflectance and its relationship to milli-scale structure. Having observed that the stratified nano-scale morphology of structurallycolored plumage contours the milli-scale cortex of the vane, I determined that measurements of the milli-scale could be substituted for a more complex study of directional reflectance from the nano-scale. I thereby hypothesize that the direction of the reflectance from a vaned feather can be predicted from the orientation of its milli-scale morphology-its barbs and barbules. In collaboration with my colleagues at Cornell University, I developed non-destructive tools and methods to investigate the signaling potential of the feather. I correlate measurements of directional light scattering to the milli-scale morphology of select samples of structurally-colored bird plumage. The results of these analyses lead to a more thorough understanding of the relationships between directional reflectance and the structure of the feather itself. Having found the reflectance to be anisotropic, I demonstrate that the change in the direction of the reflectance over the surface of the vane can in fact be predicted from the orientation of the different branches of the barb. The improved understanding of the variation in directional reflectance over the surface of the feather, a phenotypic component, should allow for better comprehension of avian behavior, evolution of morphological adaptations, and the synthesis of more accurate predictive models