Browsing by Author "Hery, Christophe"

Now showing 1 - 5 of 5
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    Accelerating Hair Rendering by Learning High-Order Scattered Radiance
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) KT, Aakash; Jarabo, Adrian; Aliaga, Carlos; Chiang, Matt Jen-Yuan; Maury, Olivier; Hery, Christophe; Narayanan, P. J.; Nam, Giljoo; Ritschel, Tobias; Weidlich, Andrea
    Efficiently and accurately rendering hair accounting for multiple scattering is a challenging open problem. Path tracing in hair takes long to converge while other techniques are either too approximate while still being computationally expensive or make assumptions about the scene. We present a technique to infer the higher order scattering in hair in constant time within the path tracing framework, while achieving better computational efficiency. Our method makes no assumptions about the scene and provides control over the renderer's bias & speedup. We achieve this by training a small multilayer perceptron (MLP) to learn the higher-order radiance online, while rendering progresses. We describe how to robustly train this network and thoroughly analyze our resulting renderer's characteristics. We evaluate our method on various hairstyles and lighting conditions. We also compare our method against a recent learning based & a traditional real-time hair rendering method and demonstrate better quantitative & qualitative results. Our method achieves a significant improvement in speed with respect to path tracing, achieving a run-time reduction of 40%-70% while only introducing a small amount of bias.
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    Efficient Path-Space Differentiable Volume Rendering With Respect To Shapes
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Yu, Zihan; Zhang, Cheng; Maury, Olivier; Hery, Christophe; Dong, Zhao; Zhao, Shuang; Ritschel, Tobias; Weidlich, Andrea
    Differentiable rendering of translucent objects with respect to their shapes has been a long-standing problem. State-of-theart methods require detecting object silhouettes or specifying change rates inside translucent objects-both of which can be expensive for translucent objects with complex shapes. In this paper, we address this problem for translucent objects with no refractive or reflective boundaries. By reparameterizing interior components of differential path integrals, our new formulation does not require change rates to be specified in the interior of objects. Further, we introduce new Monte Carlo estimators based on this formulation that do not require explicit detection of object silhouettes.
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    Gaussian Product Sampling for Rendering Layered Materials
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Xia, Mengqi (Mandy); Walter, Bruce; Hery, Christophe; Marschner, Steve; Benes, Bedrich and Hauser, Helwig
    To increase diversity and realism, surface bidirectional scattering distribution functions (BSDFs) are often modelled as consisting of multiple layers, but accurately evaluating layered BSDFs while accounting for all light transport paths is a challenging problem. Recently, Guo . [GHZ18] proposed an accurate and general position‐free Monte Carlo method, but this method introduces variance that leads to longer render time compared to non‐stochastic layered models. We improve the previous work by presenting two new sampling strategies, and . Our new methods better take advantage of the layered structure and reduce variance compared to the conventional approach of sequentially sampling one BSDF at a time. Our strategy importance samples the product of two BSDFs from a pair of adjacent layers. We further generalize this to , which importance samples the product of a chain of three or more BSDFs. In order to compute these products, we developed a new approximate Gaussian representation of individual layer BSDFs. This representation incorporates spatially varying material properties as parameters so that our techniques can support an arbitrary number of textured layers. Compared to previous Monte Carlo layering approaches, our results demonstrate substantial variance reduction in rendering isotropic layered surfaces.
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    Human Hair Inverse Rendering using Multi-View Photometric data
    (The Eurographics Association, 2021) Sun, Tiancheng; Nam, Giljoo; Aliaga, Carlos; Hery, Christophe; Ramamoorthi, Ravi; Bousseau, Adrien and McGuire, Morgan
    We introduce a hair inverse rendering framework to reconstruct high-fidelity 3D geometry of human hair, as well as its reflectance, which can be readily used for photorealistic rendering of hair. We take multi-view photometric data as input, i.e., a set of images taken from various viewpoints and different lighting conditions. Our method consists of two stages. First, we propose a novel solution for line-based multi-view stereo that yields accurate hair geometry from multi-view photometric data. Specifically, a per-pixel lightcode is proposed to efficiently solve the hair correspondence matching problem. Our new solution enables accurate and dense strand reconstruction from a smaller number of cameras compared to the state-of-the-art work. In the second stage, we estimate hair reflectance properties using multi-view photometric data. A simplified BSDF model of hair strands is used for realistic appearance reproduction. Based on the 3D geometry of hair strands, we fit the longitudinal roughness and find the single strand color. We show that our method can faithfully reproduce the appearance of human hair and provide realism for digital humans. We demonstrate the accuracy and efficiency of our method using photorealistic synthetic hair rendering data.
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    A Hyperspectral Space of Skin Tones for Inverse Rendering of Biophysical Skin Properties
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Aliaga, Carlos; Xia, Mengqi; Xie, Hao; Jarabo, Adrian; Braun, Gustav; Hery, Christophe; Ritschel, Tobias; Weidlich, Andrea
    We present a method for estimating the main properties of human skin, leveraging a hyperspectral dataset of skin tones synthetically generated through a biophysical layered skin model and Monte Carlo light transport simulations. Our approach learns the mapping between the skin parameters and diffuse skin reflectance in such space through an encoder-decoder network. We assess the performance of RGB and spectral reflectance up to 1 µm, allowing the model to retrieve visible and near-infrared. Instead of restricting the parameters to values in the ranges reported in medical literature, we allow the model to exceed such ranges to gain expressiveness to recover outliers like beard, eyebrows, rushes and other imperfections. The continuity of our albedo space allows to recover smooth textures of skin properties, enabling reflectance manipulations by meaningful edits of the skin properties. The space is robust under different illumination conditions, and presents high spectral similarity with the current largest datasets of spectral measurements of real human skin while expanding its gamut.