Light Stage - Paper Copilot

Acquiring Reflectance and Shape from Continuous Spherical Harmonic Illumination

Borom Tunwattanapong, Graham Fyffe, Paul Graham, Jay Busch, Xueming Yu, Abhijeet Ghosh, Paul Debevec

USC Institute for Creative Technologies; Imperial College London

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We present a novel technique for acquiring the geometry and spatially-varying reflectance properties of 3D objects by observing them under continuous spherical harmonic illumination conditions. The technique is general enough to characterize either entirely specular or entirely diffuse materials, or any varying combination across the surface of the object. We employ a novel computational illumination setup consisting of a rotating arc of controllable LEDs which sweep out programmable spheres of incident illumination during 1-second exposures. We illuminate the object with a succession of spherical harmonic illumination conditions, as well as photographed environmental lighting for validation. From the response of the object to the harmonics, we can separate diffuse and specular reflections, estimate world-space diffuse and specular normals, and compute anisotropic roughness parameters for each view of the object. We then use the maps of both diffuse and specular reflectance to form correspondences in a multiview stereo algorithm, which allows even highly specular surfaces to be corresponded across views. The algorithm yields a complete 3D model and a set of merged reflectance maps. We use this technique to digitize the shape and reflectance of a variety of objects difficult to acquire with other techniques and present validation renderings which match well to photographs in similar lighting.

Related Works

Spatially Varying BRDF Capture; Using Extended Light Sources; Reflectance from Spherical Illumination; Geometry from Specular Reflection

The Relightables: Volumetric Performance Capture of Humans with Realistic Relighting

Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, Matthew DuVall, Danhang Tang, Anastasia Tkach, Adarsh Kowdle, Emily Cooper, Mingsong Dou, Sean Fanello, Graham Fyffe, Christoph Rhemann, Jonathan Taylor, Paul Debevec, and Shahram Izadi

Google

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We present "The Relightables", a volumetric capture system for photorealistic and high quality relightable full-body performance capture. While significant progress has been made on volumetric capture systems, focusing on 3D geometric reconstruction with high resolution textures, much less work has been done to recover photometric properties needed for relighting. Results from such systems lack high-frequency details and the subject's shading is prebaked into the texture. In contrast, a large body of work has addressed relightable acquisition for image-based approaches, which photograph the subject under a set of basis lighting conditions and recombine the images to show the subject as they would appear in a target lighting environment. However, to date, these approaches have not been adapted for use in the context of a high-resolution volumetric capture system. Our method combines this ability to realistically relight humans for arbitrary environments, with the benefits of free-viewpoint volumetric capture and new levels of geometric accuracy for dynamic performances. Our subjects are recorded inside a custom geodesic sphere outfitted with 331 custom color LED lights, an array of high-resolution cameras, and a set of custom high-resolution depth sensors. Our system innovates in multiple areas: First, we designed a novel active depth sensor to capture 12.4MP depth maps, which we describe in detail. Second, we show how to design a hybrid geometric and machine learning reconstruction pipeline to process the high resolution input and output a volumetric video. Third, we generate temporally consistent reflectance maps for dynamic performers by leveraging the information contained in two alternating color gradient illumination images acquired at 60Hz. Multiple experiments, comparisons, and applications show that The Relightables significantly improves upon the level of realism in placing volumetrically captured human performances into arbitrary CG scenes.

Related Works

Image-based Rendering; Model Based; Volumetric Capture; Machine Learning

Efficient Multispectral Facial Capture with Monochrome Cameras

Chloe LeGendre, Kalle Bladin, Bipin Kishore, Xinglei Ren, Xueming Yu, Paul Debevec

USC Institute for Creative Technologies

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We propose a variant to polarized gradient illumination facial scanning which uses monochrome instead of color cameras to achieve more efficient and higher-resolution results. In typical polarized gradient facial scanning, sub-millimeter geometric detail is acquired by photographing the subject in eight or more polarized spherical gradient lighting conditions made with white LEDs, and RGB cameras are used to acquire color texture maps of the subject's appearance. In our approach, we replace the color cameras and white LEDs with monochrome cameras and multispectral, colored LEDs, leveraging that color images can be formed from successive monochrome images recorded under different illumination colors. While a naive extension of the scanning process to this setup would require multiplying the number of images by number of color channels, we show that the surface detail maps can be estimated directly from monochrome imagery, so that only an additional n photographs are required, where n is the number of added spectral channels. We also introduce a new multispectral optical flow approach to align images across spectral channels in the presence of slight subject motion. Lastly, for the case where a capture system's white light sources are polarized and its multispectral colored LEDs are not, we introduce the technique of multispectral polarization promotion, where we estimate the cross- and parallel-polarized monochrome images for each spectral channel from their corresponding images under a full sphere of even, unpolarized illumination. We demonstrate that this technique allows us to efficiently acquire a full color (or even multispectral) facial scan using monochrome cameras, unpolarized multispectral colored LEDs, and polarized white LEDs.

The Light Stages and Their Applications to Photoreal Digital Actors

Paul Debevec

University of Southern California Institute for Creative Technologies

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The Light Stage systems built at UC Berkeley and USC ICT have enabled a variety of facial scanning and reflectance measurement techniques that have been explored in several research papers and used in various commercial applications. This short paper presents the evolutionary history of the Light Stage Systems and some of the techniques and applications they have enabled.

Estimating Specular Roughness and Anisotropy from Second Order Spherical Gradient Illumination

Abhijeet Ghosh, Tongbo Chen, Pieter Peers, Cyrus A. Wilson, Paul Debevec

University of Southern California, Institute for Creative Technologies

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We present a novel method for estimating specular roughness and tangent vectors, per surface point, from polarized second order spherical gradient illumination patterns. We demonstrate that for isotropic BRDFs, only three second order spherical gradients are sufficient to robustly estimate spatially varying specular roughness. For anisotropic BRDFs, an additional two measurements yield specular roughness and tangent vectors per surface point. We verify our approach with different illumination configurations which project both discrete and continuous fields of gradient illumination. Our technique provides a direct estimate of the per-pixel specular roughness and thus does not require off-line numerical optimization that is typical for the measure-and-fit approach to classical BRDF modeling.

Related Works

Analytical BRDF-based Methods; Non-parametric and Photometric Techniques

Multiview Face Capture using Polarized Spherical Gradient Illumination

Abhijeet Ghosh, Graham Fyffe, Borom Tunwattanapong, Jay Busch, Xueming Yu, Paul Debevec

USC Institute for Creative Technologies

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We present a novel process for acquiring detailed facial geometry with high resolution diffuse and specular photometric information from multiple viewpoints using polarized spherical gradient illumination. Key to our method is a new pair of linearly polarized lighting patterns which enables multiview diffuse-specular separation under a given spherical illumination condition from just two photographs. The patterns -- one following lines of latitude and one following lines of longitude -- allow the use of fixed linear polarizers in front of the cameras, enabling more efficient acquisition of diffuse and specular albedo and normal maps from multiple viewpoints. In a second step, we employ these albedo and normal maps as input to a novel multi-resolution adaptive domain message passing stereo reconstruction algorithm to create high resolution facial geometry. To do this, we formulate the stereo reconstruction from multiple cameras in a commonly parameterized domain for multiview reconstruction. We show competitive results consisting of high-resolution facial geometry with relightable reflectance maps using five DSLR cameras. Our technique scales well for multiview acquisition without requiring specialized camera systems for sensing multiple polarization states.

Related Works

3D Facial Capture; Spherical Gradient Illumination

Comparisons

Fyffe et al.

Single-Shot Reflectance Measurement from Polarized Color Gradient Illumination

Graham Fyffe, Paul Debevec

USC Institute for Creative Technologies

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Abstract

We present a method for acquiring the per-pixel diffuse albedo, specular albedo, and surface normal maps of a subject at a single instant in time. The method is singleshot, requiring no optical flow, and per-pixel, making no assumptions regarding albedo statistics or surface connectivity. We photograph the subject inside a spherical illumination device emitting a static lighting pattern of vertically polarized RGB color gradients aligned with the XYZ axes, and horizontally polarized RGB color gradients inversely aligned with the XYZ axes. We capture simultaneous photographs using one of two possible setups: a singleview setup using a coaxially aligned camera pair with a polarizing beam splitter, and a multi-view stereo setup with different orientations of linear polarizing filters placed on the cameras, enabling high-quality geometry reconstruction. From this lighting we derive full-color diffuse albedo, single-channel specular albedo suitable for dielectric materials, and polarization-preserving surface normals which are free of corruption from subsurface scattering. We provide simple formulae to estimate the diffuse albedo, specular albedo, and surface normal maps in the single-view and multi-view cases and show error bounds which are small for many common subjects including faces.

Related Works

Reflectance measurement

Rapid Acquisition of Specular and Diffuse Normal Maps from Polarized Spherical Gradient Illumination

Wan-Chun Ma, Tim Hawkins, Pieter Peers, Charles-Felix Chabert, Malte Weiss, Paul Debevec

University of Southern California Institute for Creative Technologies

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Abstract

We estimate surface normal maps of an object from either its diffuse or specular reflectance using four spherical gradient illumination patterns. In contrast to traditional photometric stereo, the spherical patterns allow normals to be estimated simultaneously from any number of viewpoints. We present two polarized lighting techniques that allow the diffuse and specular normal maps of an object to be measured independently. For scattering materials, we show that the specular normal maps yield the best record of detailed surface shape while the diffuse normals deviate from the true surface normal due to subsurface scattering, and that this effect is dependent on wavelength. We show several applications of this acquisition technique. First, we capture normal maps of a facial performance simultaneously from several viewing positions using time-multiplexed illumination. Second, we show that highresolution normal maps based on the specular component can be used with structured light 3D scanning to quickly acquire high-resolution facial surface geometry using off-the-shelf digital still cameras. Finally, we present a realtime shading model that uses independently estimated normal maps for the specular and diffuse color channels to reproduce some of the perceptually important effects of subsurface scattering.

Related Works

Reflectance Scanning Techniques; Photometric Stereo and Extensions; Polarization-Based Analysis of Reflected Light

Acquiring the Reflectance Field of a Human Face

Paul Debevec,Tim Hawkins,Chris Tchou,Haarm-Pieter Duiker,Westley Sarokin,Mark Sagar

University of California at Berkeley; LifeF/X, Inc.

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Abstract

We present a method to acquire the reflectance field of a human face and use these measurements to render the face under arbitrary changes in lighting and viewpoint. We first acquire images of the face from a small set of viewpoints under a dense sampling of incident illumination directions using a light stage. We then construct a reflectance function image for each observed image pixel from its values over the space of illumination directions. From the reflectance functions, we can directly generate images of the face from the original viewpoints in any form of sampled or computed illumination. To change the viewpoint, we use a model of skin reflectance to estimate the appearance of the reflectance functions for novel viewpoints. We demonstrate the technique with synthetic renderings of a person's face under novel illumination and viewpoints.

Related Works

Facial Modeling and Animation; Reflectometry; Image-Based Modeling and Rendering

Deep Reflectance Fields: High-Quality Facial Reflectance Field Inference From Color Gradient Illumination

Abhimitra Meka, Christian H

MPI Informatics; Saarland Informatics Campus; Google; Stanford University

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Abstract

We present a novel technique to relight images of human faces by learning a model of facial reflectance from a database of 4D reflectance field data of several subjects in a variety of expressions and viewpoints. Using our learned model, a face can be relit in arbitrary illumination environments using only two original images recorded under spherical color gradient illumination. The output of our deep network indicates that the color gradient images contain the information needed to estimate the full 4D reflectance field, including specular reflections and high frequency details. While capturing spherical color gradient illumination still requires a special lighting setup, reduction to just two illumination conditions allows the technique to be applied to dynamic facial performance capture. We show side-by-side comparisons which demonstrate that the proposed system outperforms the state-of-the-art techniques in both realism and speed.

Related Works

Parametric Model Fitting; Image-Based Relighting; Learning-Based Techniques

Comparisons

Fyffe et al. 2009, Shu et al. 2017, Yamaguchi et al. 2018

Category: Light Stage

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