Fitting procedural yarn models for realistic cloth rendering

Zhao, Shuang; Luan, Fujun; Bala, Kavita

doi:10.1145/2897824.2925932

Cited by 51 publications

(63 citation statements)

References 20 publications

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“…Modeling cloth at the fiber level is a growing area of research with remarkable recent progress [Leaf et al 2018;Zhao et al 2016]; the explicit fiber representation can be used for high-fidelity rendering, but the cost is prohibitive in most applications. The situation is different in our case, where the cost is incurred only in dataset creation, not in training or rendering.…”

Section: Fabric Gndfsmentioning

confidence: 99%

Learning generative models for rendering specular microgeometry

Кuznetsov

Hašan

et al. 2019

ACM Trans. Graph.

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from synthetic or measured examples, by training a generative adversarial network (GAN). A key challenge in applying GAN synthesis to spatially varying BRDFs is evaluating the reflectance for a single location and direction without the cost of evaluating the whole hemisphere. We resolve this using a novel method for partial evaluation of the generator network. We are also able to control large-scale spatial texture using a conditional GAN approach. The benefits of our approach include the ability to synthesize spatially large results without repetition, support for learning from measured data, and evaluation performance independent of the complexity of the dataset synthesis or measurement. CCS Concepts: • Computing methodologies → Rendering.

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Section: Fabric Gndfsmentioning

confidence: 99%

Learning generative models for rendering specular microgeometry

Кuznetsov

Hašan

et al. 2019

ACM Trans. Graph.

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“…Their fully procedural fiber generation method even simulates the small fibers protruding from the yarn (hairiness) and allows predictive reverse engineering of real cloth. Zhao et al [ZLB16] automated the fitting of such yarn procedural models from physical measurements acquired using micro CT imaging. Recently, Wu and Yuksel [WY17] demonstrated real-time rendering of cloth modeled using explicit yarns.…”

Section: Related Workmentioning

confidence: 99%

An Appearance Model for Textile Fibers

Aliaga

Castillo

Gutiérrez

et al. 2017

Computer Graphics Forum

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Figure 1: Volumetric rendering of three fabric samples using our highly-detailed physically-based BCSDFs models. Our model is based on the optical and structural parameters particular of each cloth fiber. From left to right, a 2-ply knitted garter (7 twist/cm, 120 fibers/yarn) of cotton fibers, a single-ply woven satin made of silk (1 twist/cm, 30 fibers/yarn) and a 2-ply knitted garter (4 twist/cm, 160 fibers/yarn) made of polyester fibers. Silk and cotton pieces are colored with reactive dyes, and a disperse dye is used for polyester. AbstractAccurately modeling how light interacts with cloth is challenging, due to the volumetric nature of cloth appearance and its multiscale structure, where microstructures play a major role in the overall appearance at higher scales. Recently, significant effort has been put on developing better microscopic models for cloth structure, which have allowed rendering fabrics with unprecedented fidelity. However, these highly-detailed representations still make severe simplifications on the scattering by individual fibers forming the cloth, ignoring the impact of fibers' shape, and avoiding to establish connections between the fibers' appearance and their optical and fabrication parameters. In this work we put our focus in the scattering of individual cloth fibers; we introduce a physically-based scattering model for fibers based on their low-level optical and geometric properties, relying on the extensive textile literature for accurate data. We demonstrate that scattering from cloth fibers exhibits much more complexity than current fiber models, showing important differences between cloth type, even in averaged conditions due to longer views. Our model can be plugged in any framework for cloth rendering, matches scattering measurements from real yarns, and is based on actual parameters used in the textile industry, allowing predictive bottom-up definition of cloth appearance.

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“…as opposed to knitted textiles) and removes the requirement on CT scan data [Zhao et al 2012]. In [Zhao et al 2016] Zhao et al…”

Section: Noise Estimationmentioning

confidence: 99%

Woven Fabric Model Creation from a Single Image

Guarnera

Hall

Chesnais³

et al. 2017

ACM Trans. Graph.

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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., yarn width, 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, the woven 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.

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Fitting procedural yarn models for realistic cloth rendering

Cited by 51 publications

References 20 publications

Learning generative models for rendering specular microgeometry

Learning generative models for rendering specular microgeometry

An Appearance Model for Textile Fibers

Woven Fabric Model Creation from a Single Image

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