Material Representation - Paper Copilot

MIPNet: Neural Normal-to-Anisotropic-Roughness MIP mapping

Alban Gauthier, Robin Faury, Jérémy Levallois, Théo Thonat, Jean-Marc Thiery, Tamy Boubekeur

Adobe; Télécom Paris

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We present MIPNet, a novel approach for SVBRDF mipmapping which preserves material appearance under varying view distances and lighting conditions. As in classical mipmapping, our method explicitly encodes the multiscale appearance of materials in a SVBRDF mipmap pyramid. To do so, we use a tensor-based representation, coping with gradient-based optimization, for encoding anisotropy which is compatible with existing real-time rendering engines. Instead of relying on a simple texture patch average for each channel independently, we propose a cascaded architecture of multilayer perceptrons to approximate the material appearance using only the fixed material channels. Our neural model learns simple mipmapping filters using a differentiable rendering pipeline based on a rendering loss and is able to transfer signal from normal to anisotropic roughness. As a result, we obtain a drop-in replacement for standard material mipmapping, offering a significant improvement in appearance preservation while still boiling down to a single per-pixel mipmap texture fetch. We report extensive experiments on two distinct BRDF models.

Related Works

Texture minification; Normal map filtering; Rendering high-resolution normal maps; Reflectance filtering; Differentiable rendering

Metappearance: Meta-Learning for Visual Appearance Reproduction

Michael Fischer, Tobias Ritschel

University College London

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There currently exist two main approaches to reproducing visual appearance using Machine Learning (ML): The first is training models that generalize over different instances of a problem, e.g., different images of a dataset. As one-shot approaches, these offer fast inference, but often fall short in quality. The second approach does not train models that generalize across tasks, but rather over-fit a single instance of a problem, e.g., a flash image of a material. These methods offer high quality, but take long to train. We suggest to combine both techniques end-to-end using meta-learning: We over-fit onto a single problem instance in an inner loop, while also learning how to do so efficiently in an outer-loop across many exemplars. To this end, we derive the required formalism that allows applying meta-learning to a wide range of visual appearance reproduction problems: textures, BRDFs, svBRDFs, illumination or the entire light transport of a scene. The effects of meta-learning parameters on several different aspects of visual appearance are analyzed in our framework, and specific guidance for different tasks is provided. Metappearance enables visual quality that is similar to over-fit approaches in only a fraction of their runtime while keeping the adaptivity of general models.

Related Works

Visual Appearance; Learning; General Learning; Over-fit Optimization; Fine-tuning; Hyper- and meta-learning

NeuSample: Importance Sampling for Neural Materials

Bing Xu, Liwen Wu, Milos Hasan, Fujun Luan, Iliyan Georgiev, Zexiang Xu, Ravi Ramamoorthi

University of California, San Diego; Adobe Research

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Neural material representations have recently been proposed to augment the material appearance toolbox used in realistic rendering. These models are successful at tasks ranging from measured BTF compression, through efficient rendering of synthetic displaced materials with occlusions, to BSDF layering. However, importance sampling has been an after-thought in most neural material approaches, and has been handled by inefficient cosine-hemisphere sampling or mixing it with an additional simple analytic lobe. In this paper we fill that gap, by evaluating and comparing various pdf-learning approaches for sampling spatially varying neural materials, and proposing new variations of these approaches. We investigate three sampling approaches: analytic-lobe mixtures, normalizing flows, and histogram prediction. Within each type, we introduce improvements beyond previous work, and we extensively evaluate and compare these approaches in terms of sampling rate, wall-clock time, and final visual quality. Our versions of normalizing flows and histogram mixtures perform well and can be used in practical rendering systems, potentially facilitating the broader adoption of neural material models in production.

Related Works

Neural materials; Analytic approximations; Normalizing flows; Histogram prediction

Neural Biplane Representation for BTF Rendering and Acquisition

Jiahui Fan, Beibei Wang, Milos Hasan, Jian Yang, Ling-Qi Yan

Nanjing University of Science and Technology; Nankai University; Adobe Research; University of California, Santa Barbara

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Bidirectional Texture Functions (BTFs) are able to represent complex materials with greater generality than traditional analytical models. This holds true for both measured real materials and synthetic ones. Recent advancements in neural BTF representations have significantly reduced storage costs, making them more practical for use in rendering. These representations typically combine spatial feature (latent) textures with neural decoders that handle angular dimensions per spatial location. However, these models have yet to combine fast compression and inference, accuracy, and generality. In this paper, we propose a biplane representation for BTFs, which uses a feature texture in the half-vector domain as well as the spatial domain. This allows the learned representation to encode high-frequency details in both the spatial and angular domains. Our decoder is small yet general, meaning it is trained once and fixed. Additionally, we optionally combine this representation with a neural offset module for parallax and masking effects. Our model can represent a broad range of BTFs and has fast compression and inference due to its lightweight architecture. Furthermore, it enables a simple way to capture BTF data. By taking about 20 cell phone photos with a collocated camera and flash, our model can plausibly recover the entire BTF, despite never observing function values with differing view and light directions. We demonstrate the effectiveness of our model in the acquisition of many measured materials, including challenging materials such as fabrics.

Related Works

Neural based BTF/BRDF compression; Exhaustive BTF capture; Lightweight neural SVBRDF acquisition

Towards Material Digitization With a Dual-scale Optical System

Elena Garces, Victor Arellano, Carlos Rodriguez-Pardo, David Pascual, Sergio Suja, Jorge Lopez-Moreno

Universidad Rey Juan Carlos; SEDDI

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Existing devices for measuring material appearance in spatially-varying samples are limited to a single scale, either micro or mesoscopic. This is a practical limitation when the material has a complex multi-scale structure. In this paper, we present a system and methods to digitize materials at two scales, designed to include high-resolution data in spatially-varying representations at larger scales. We design and build a hemispherical light dome able to digitize flat material samples up to 11x11cm. We estimate geometric properties, anisotropic reflectance and transmittance at the microscopic level using polarized directional lighting with a single orthogonal camera. Then, we propagate this structured information to the mesoscale, using a neural network trained with the data acquired by the device and image-to-image translation methods. To maximize the compatibility of our digitization, we leverage standard BSDF models commonly adopted in the industry. Through extensive experiments, we demonstrate the precision of our device and the quality of our digitization process using a set of challenging real-world material samples and validation scenes. Further, we demonstrate the optical resolution and potential of our device for acquiring more complex material representations by capturing microscopic attributes which affect the global appearance: we characterize the properties of textile materials such as the yarn twist or the shape of individual fly-out fibers. We also release the SEDDIDOME dataset of materials, including raw data captured by the machine and optimized parameteres.

Related Works

Rigid Capture Devices; Lightweight Capture Systems; Fiber-Level Capture; Translucency

End-to-end Procedural Material Capture With Proxy-free Mixed-integer Optimization

Beichen Li, Liang Shi, Wojciech Matusik

MIT CSAIL

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Node-graph-based procedural materials are vital to 3D content creation within the computer graphics industry. Leveraging the expressive representation of procedural materials, artists can effortlessly generate diverse appearances by altering the graph structure or node parameters. However, manually reproducing a specific appearance is a challenging task that demands extensive domain knowledge and labor. Previous research has sought to automate this process by converting artist-created material graphs into differentiable programs and optimizing node parameters against a photographed material appearance using gradient descent. These methods involve implementing differentiable filter nodes [Shi et al. 2020] and training differentiable neural proxies for generator nodes to optimize continuous and discrete node parameters [Hu et al. 2022a] jointly. Nevertheless, Neural Proxies exhibits critical limitations, such as long training times, inaccuracies, fixed resolutions, and confined parameter ranges, which hinder their scalability towards the broad spectrum of production-grade material graphs. These constraints fundamentally stem from the absence of faithful and efficient implementations of generic noise and pattern generator nodes, both differentiable and non-differentiable. Such deficiency prevents the direct optimization of continuous and discrete generator node parameters without relying on surrogate models. We present Diffmat v2, an improved differentiable procedural material library, along with a fully-automated, end-to-end procedural material capture framework that combines gradient-based optimization and gradient-free parameter search to match existing production-grade procedural materials against user-taken flash photos. Diffmat v2 expands the range of differentiable material graph nodes in Diffmat [Shi et al. 2020] by adding generic noise/pattern generator nodes and user-customizable per-pixel filter nodes. This allows for the complete translation and optimization of procedural materials across various categories without the need for external proprietary tools or pre-cached noise patterns. Consequently, our method can capture a considerably broader array of materials, encompassing those with highly regular or stochastic geometries. We demonstrate that our end-to-end approach yields a closer match to the target than MATch [Shi et al. 2020] and Neural Proxies [Hu et al. 2022a] when starting from initially unmatched continuous and discrete parameters.

Related Works

Procedural noise; Inverse procedural appearance capture

Comparisons

MATch, Neural Proxies

SpongeCake: A Layered Microflake Surface Appearance Model

Beibei Wang, Wenhua Jin, Miloš Hašan, Ling-Qi Yan

Nankai University; Nanjing University of Science and Technology; Adobe Research; University of California, Santa Barbara

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In this paper, we propose SpongeCake: a layered BSDF model where each layer is a volumetric scattering medium, defined using microflake or other phase functions. We omit any reflecting and refracting interfaces between the layers. The first advantage of this formulation is that an exact and analytic solution for single scattering, regardless of the number of volumetric layers, can be derived. We propose to approximate multiple scattering by an additional single-scattering lobe with modified parameters and a Lambertian lobe. We use a parameter mapping neural network to find the parameters of the newly added lobes to closely approximate the multiple scattering effect. Despite the absence of layer interfaces, we demonstrate that many common material effects can be achieved with layers of SGGX microflake and other volumes with appropriate parameters. A normal mapping effect can also be achieved through mapping of microflake orientations, which avoids artifacts common in standard normal maps. Thanks to the analytical formulation, our model is very fast to evaluate and sample. Through various parameter settings, our model is able to handle many types of materials, like plastics, wood, cloth, etc., opening a number of practical applications.

Related Works

Layered material models; Microflake models; Microfacet multiple scattering; Single scattering in volumetric layers; Neural networks for material appearance

A Flexible Neural Renderer for Material Visualization

Aakash KT, Parikshit Sakurikar, Saurabh Saini, P. J. Narayanan

IIIT Hyderabad; DreamVu Inc.

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Photo realism in computer generated imagery is crucially dependent on how well an artist is able to recreate real-world materials in the scene. The workflow for material modeling and editing typically involves manual tweaking of material parameters and uses a standard path tracing engine for visual feedback. A lot of time may be spent in iterative selection and rendering of materials at an appropriate quality. In this work, we propose a convolutional neural network based workflow which quickly generates high-quality ray traced material visualizations on a shaderball. Our novel architecture allows for control over environment lighting and assists material selection along with the ability to render spatially-varying materials. Additionally, our network enables control over environment lighting which gives an artist more freedom and provides better visualization of the rendered material. Comparison with state-of-the-art denoising and neural rendering techniques suggests that our neural renderer performs faster and better. We provide a interactive visualization tool and release our training dataset to foster further research in this area.

Related Works

Material modelling; Material acquisition; Rendering as Denoising; Image-based relighting; Neural rendering

MII: Modeling Indirect Illumination for Inverse Rendering

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Category: Material Representation

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