Computer-generated imagery is now ubiquitous in our society, spanning fields such as games and movies, architecture, engineering, or virtual prototyping, while also helping create novel ones such as computational materials. With the increase in computational power and the improvement of acquisition techniques, there has been a paradigm shift in the field towards data-driven techniques, which has yielded an unprecedented level of realism in visual appearance.
Unfortunately, this leads to a series of problems: First, there is a disconnect between the mathematical representation of the data and any meaningful parameters that humans understand; the captured data is machine-friendly, but not human friendly. Second, the many different acquisition systems lead to heterogeneous formats and very large datasets. And third, real world appearance functions are usually nonlinear and high-dimensional. As a result, visual appearance datasets are increasingly unfit to editing operations, which limits the creative process for scientists, engineers, artists and practitioners in general. There is an immense gap between the complexity, realism and richness of the captured data, and the flexibility to edit such data. This line of research plans to bridge this gap, putting the user at the core. Achieving our goals will finally enable us to reach the true potential of real-world captured datasets in many aspects of society.
Abstract: We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived appearance similarity. Our evaluation shows that our model outperforms existing metrics. Last, we demonstrate several applications enabled by our metric, including appearance-based search for material suggestions, database visualization, clustering and summarization, and gamut mapping.
Abstract: We analyze the effect of motion in the perception of material appearance. First, we create a set of stimuli containing 72 realistic materials, rendered with varying degrees of linear motion blur. Then we launch a large-scale study on Mechanical Turk to rate a given set of perceptual attributes, such as brightness, roughness, or the perceived strength of reflections. Our statistical analysis shows that certain attributes undergo a significant change, varying appearance perception under motion. In addition, we further investigate the perception of brightness, for the particular cases of rubber and plastic materials. We create new stimuli, with ten different luminance levels and seven motion degrees. We launch a new user study to retrieve their perceived brightness. From the users’ judgements, we build two-dimensional maps showing how perceived brightness varies as a function of the luminance and motion of the material.
Abstract: Accurately 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.
Abstract: Reproducing the appearance of real-world materials using current printing technology is problematic. The reduced number of inks available define the printer’s limited gamut, creating distortions in the printed appearance that are hard to control. Gamut mapping refers to the process of bringing an out-of-gamut material appearance into the printer’s gamut, while minimizing such distortions as much as possible. We present a novel two-step gamut mapping algorithm that allows users to specify which perceptual attribute of the original material they want to preserve (such as brightness, or roughness). In the first step, we work in the low-dimensional intuitive appearance space recently proposed by Serrano et al., and adjust achromatic reflectance via an objective function that strives to preserve certain attributes. From such intermediate representation, we then perform an image-based optimization including color information, to bring the BRDF into gamut. We show, both objectively and through a user study, how our method yields superior results compared to the state of the art, with the additional advantage that the user can specify which visual attributes need to be preserved. Moreover, we show how this approach can also be used for attribute-preserving material editing.
Abstract: During the last few years, many different techniques for measuring material appearance have arisen. These advances have allowed the creation of large public datasets, and new methods for editing BRDFs of captured appearance have been proposed. However, these methods lack intuitiveness and are hard to use for novice users. In order to overcome these limitations, Serrano et al. recently proposed an intuitive space for editing captured appearance. They make use of a representation of the BRDF based on a combination of principal components (PCA) to reduce dimensionality, and then map these components to perceptual attributes. This PCA representation is biased towards specular materials and fails to represent very diffuse BRDFs, therefore producing unpleasant artifacts when editing. In this paper, we build on top of their work and propose to use two separate PCA bases for representing specular and diffuse BRDFs, and map each of these bases to the perceptual attributes. This allows us to avoid artifacts when editing towards diffuse BRDFs. We then propose a new method for effectively navigate between both bases while editing based on a new measurement of the specularity of measured materials. Finally, we integrate our proposed method in an intuitive BRDF editing framework and show how some of the limitations of the previous model have been overcomed with our representation. Moreover, our new measure of specularity can be used on any measured BRDF, as it is not limited only to MERL BRDFs.
Abstract: Many different techniques for measuring material appearance have been proposed in the last few years. These have produced large public datasets, which have been used for accurate, data-driven appearance modeling. However, although these datasets have allowed us to reach an unprecedented level of realism in visual appearance, editing the captured data remains a challenge. In this paper, we present an intuitive control space for predictable editing of captured BRDF data, which allows for artistic creation of plausible novel material appearances, bypassing the difficulty of acquiring novel samples. We first synthesize novel materials, extending the existing MERL dataset up to 400 mathematically valid BRDFs. We then design a large-scale experiment, gathering 56,000 subjective ratings on the high-level perceptual attributes that best describe our extended dataset of materials. Using these ratings, we build and train networks of radial basis functions to act as functionals mapping the perceptual attributes to an underlying PCA-based representation of BRDFs. We show that our functionals are excellent predictors of the perceived attributes of appearance. Our control space enables many applications, including intuitive material editing of a wide range of visual properties, guidance for gamut mapping, analysis of the correlation between perceptual attributes, or novel appearance similarity metrics. Moreover, our methodology can be used to derive functionals applicable to classic analytic BRDF representations. We release our code and dataset publicly, in order to support and encourage further research in this direction.