Face Relighting - Paper Copilot

Face relighting with radiance environment maps

Zhen Wen, Zicheng Liu, Thomas S. Huang

University of Illinois Urbana; Microsoft Research Redmond

Portals

Abstract

A radiance environment map pre-integrates a constant surface reflectance with the lighting environment. It has been used to generate photo-realistic rendering at interactive speed. However, one of its limitations is that each radiance environment map can only render the object, which has the same surface reflectance as what it integrates. We present a ratio-image based technique to use a radiance environment map to render diffuse objects with different surface reflectance properties. This method has the advantage that it does not require the separation of illumination from reflectance, and it is simple to implement and runs at interactive speed. In order to use this technique for human face relighting, we have developed a technique that uses spherical harmonics to approximate the radiance environment map for any given image of a face. Thus we are able to relight face images when the lighting environment rotates. Another benefit of the radiance environment map is that we can interactively modify lighting by changing the coefficients of the spherical harmonics basis. Finally we can modify the lighting condition of one person's face so that it matches the new lighting condition of a different person's face image assuming the two faces have similar skin albedos.

NeLF: Neural Light-transport Field for Portrait View Synthesis and Relighting

Tiancheng Sun, Kai-En Lin, Sai Bi, Zexiang Xu, Ravi Ramamoorthi

University of California, San Diego; Adobe Research

Portals

Abstract

Human portraits exhibit various appearances when observed from different views under different lighting conditions. We can easily imagine how the face will look like in another setup, but computer algorithms still fail on this problem given limited observations. To this end, we present a system for portrait view synthesis and relighting: given multiple portraits, we use a neural network to predict the light-transport field in 3D space, and from the predicted Neural Light-transport Field (NeLF) produce a portrait from a new camera view under a new environmental lighting. Our system is trained on a large number of synthetic models, and can generalize to different synthetic and real portraits under various lighting conditions. Our method achieves simultaneous view synthesis and relighting given multi-view portraits as the input, and achieves state-of-the-art results.

Related Works

Portrait Appearance; Relighting; View Synthesis; Neural Rendering

Comparisons

SIPR, IBRNet

Neural Light Transport for Relighting and View Synthesis

Xiuming Zhang, Sean Fanello, Yun-Ta Tsai, Tiancheng Sun, Tianfan Xue, Rohit Pandey, Sergio Orts-Escolano, Philip Davidson, Christoph Rhemann, Paul Debevec, Jonathan T. Barron, Ravi Ramamoorthi, William T. Freeman

Massachusetts Institute of Technology; Google; University of California, San Diego

Portals

Abstract

The light transport (LT) of a scene describes how it appears under different lighting and viewing directions, and complete knowledge of a scene's LT enables the synthesis of novel views under arbitrary lighting. In this paper, we focus on image-based LT acquisition, primarily for human bodies within a light stage setup. We propose a semi-parametric approach to learn a neural representation of LT that is embedded in the space of a texture atlas of known geometric properties, and model all non-diffuse and global LT as residuals added to a physically-accurate diffuse base rendering. In particular, we show how to fuse previously seen observations of illuminants and views to synthesize a new image of the same scene under a desired lighting condition from a chosen viewpoint. This strategy allows the network to learn complex material effects (such as subsurface scattering) and global illumination, while guaranteeing the physical correctness of the diffuse LT (such as hard shadows). With this learned LT, one can relight the scene photorealistically with a directional light or an HDRI map, synthesize novel views with view-dependent effects, or do both simultaneously, all in a unified framework using a set of sparse, previously seen observations. Qualitative and quantitative experiments demonstrate that our neural LT (NLT) outperforms state-of-the-art solutions for relighting and view synthesis, without separate treatment for both problems that prior work requires.

Related Works

Single observation; Multiple views; Multiple illuminants; Multiple views and illuminants

Comparisons

Diffuse Base, Barycentric Blending, Deep Shading, Xu et al., Relightables

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

Portals

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

Face Relighting with Geometrically Consistent Shadows

Andrew Hou, Michel Sarkis, Ning Bi, Yiying Tong, Xiaoming Liu

Michigan State University; Qualcomm Technologies Inc.

Portals

Abstract

Most face relighting methods are able to handle diffuse shadows, but struggle to handle hard shadows, such as those cast by the nose. Methods that propose techniques for handling hard shadows often do not produce geometrically consistent shadows since they do not directly leverage the estimated face geometry while synthesizing them. We propose a novel differentiable algorithm for synthesizing hard shadows based on ray tracing, which we incorporate into training our face relighting model. Our proposed algorithm directly utilizes the estimated face geometry to synthesize geometrically consistent hard shadows. We demonstrate through quantitative and qualitative experiments on Multi-PIE and FFHQ that our method produces more geometrically consistent shadows than previous face relighting methods while also achieving state-of-the-art face relighting performance under directional lighting. In addition, we demonstrate that our differentiable hard shadow modeling improves the quality of the estimated face geometry over diffuse shading models.

Related Works

Face Relighting; Differentiable Rendering and Ray Tracing

Comparisons

SfSNet, DPR, SIPR, Nestmeyer

Category: Face Relighting

Portals

Abstract

Portals

Abstract

Related Works

Comparisons

Portals

Abstract

Related Works

Comparisons

Portals

Abstract

Related Works

Comparisons

Portals

Abstract

Related Works

Comparisons