Band-Sifting Decomposition for Image-Based Material Editing

Photographers often "prep" their subjects to achieve various effects; for example, toning down overly shiny skin, covering blotches, etc. Making such adjustments digitally after a shoot is possible, but difficult without good tools and good skills. Making such adjustments to video footage is harder still. We describe and study a set of 2D image operations, based on multiscale image analysis, that are easy and straightforward and that can consistently modify perceived material properties. These operators first build a subband decomposition of the image and then selectively modify the coefficients within the subbands. We call this selection process band sifting. We show that different siftings of the coefficients can be used to modify the appearance of properties such as gloss, smoothness, pigmentation, or weathering. The band-sifting operators have particularly striking effects when applied to faces; they can provide "knobs" to make a face look wetter or drier, younger or older, and with heavy or light variation in pigmentation. Through user studies, we identify a set of operators that yield consistent subjective effects for a variety of materials and scenes. We demonstrate that these operators are also useful for processing video sequences

An intuitive control space for material appearance

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

An intuitive control space for material appearance

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

A generative framework for image-based editing of material appearance using perceptual attributes

Single-image appearance editing is a challenging task, traditionally requiring the estimation of additional scene properties such as geometry or illumination. Moreover, the exact interaction of light, shape and material reflectance that elicits a given perceptual impression is still not well understood. We present an image-based editing method that allows to modify the material appearance of an object by increasing or decreasing high-level perceptual attributes, using a single image as input. Our framework relies on a two-step generative network, where the first step drives the change in appearance and the second produces an image with high-frequency details. For training, we augment an existing material appearance dataset with perceptual judgements of high-level attributes, collected through crowd-sourced experiments, and build upon training strategies that circumvent the cumbersome need for original-edited image pairs. We demonstrate the editing capabilities of our framework on a variety of inputs, both synthetic and real, using two common perceptual attributes (Glossy and Metallic), and validate the perception of appearance in our edited images through a user study

In-the-wild Material Appearance Editing using Perceptual Attributes

Intuitively editing the appearance of materials from a single image is a challenging task given the complexity of the interactions between light and matter, and the ambivalence of human perception. This problem has been traditionally addressed by estimating additional factors of the scene like geometry or illumination, thus solving an inverse rendering problem and subduing the final quality of the results to the quality of these estimations. We present a single-image appearance editing framework that allows us to intuitively modify the material appearance of an object by increasing or decreasing high-level perceptual attributes describing such appearance (e.g., glossy or metallic). Our framework takes as input an in-the-wild image of a single object, where geometry, material, and illumination are not controlled, and inverse rendering is not required. We rely on generative models and devise a novel architecture with Selective Transfer Unit (STU) cells that allow to preserve the high-frequency details from the input image in the edited one. To train our framework we leverage a dataset with pairs of synthetic images rendered with physically-based algorithms, and the corresponding crowd-sourced ratings of high-level perceptual attributes. We show that our material editing framework outperforms the state of the art, and showcase its applicability on synthetic images, in-the-wild real-world photographs, and video sequences