We introduce InverseFaceNet, a deep convolutional inverse rendering framework
for faces that jointly estimates facial pose, shape, expression, reflectance
and illumination from a single input image. By estimating all parameters from
just a single image, advanced editing possibilities on a single face image,
such as appearance editing and relighting, become feasible in real time. Most
previous learning-based face reconstruction approaches do not jointly recover
all dimensions, or are severely limited in terms of visual quality. In
contrast, we propose to recover high-quality facial pose, shape, expression,
reflectance and illumination using a deep neural network that is trained using
a large, synthetically created training corpus. Our approach builds on a novel
loss function that measures model-space similarity directly in parameter space
and significantly improves reconstruction accuracy. We further propose a
self-supervised bootstrapping process in the network training loop, which
iteratively updates the synthetic training corpus to better reflect the
distribution of real-world imagery. We demonstrate that this strategy
outperforms completely synthetically trained networks. Finally, we show
high-quality reconstructions and compare our approach to several
state-of-the-art approaches.

InverseFaceNet 是一种基于深度卷积反渲染框架的新方法，可以实时地从单个输入图像中同时估计人脸的姿态、形状、表情、反射和照明，通过利用深度神经网络训练，结合实时的外观编辑和重新照明等高级处理技术，实现了高质量、高度还原的人脸重建。

InverseFaceNet: 深度单目逆向人脸渲染

InverseFaceNet: Deep Monocular Inverse Face Rendering

Face alignment aims to estimate the locations of a set of landmarks for a
given image. This problem has received much attention as evidenced by the
recent advancement in both the methodology and performance. However, most of
the existing works neither explicitly handle face images with arbitrary poses,
nor perform large-scale experiments on non-frontal and profile face images. In
order to address these limitations, this paper proposes a novel face alignment
algorithm that estimates both 2D and 3D landmarks and their 2D visibilities for
a face image with an arbitrary pose. By integrating a 3D deformable model, a
cascaded coupled-regressor approach is designed to estimate both the camera
projection matrix and the 3D landmarks. Furthermore, the 3D model also allows
us to automatically estimate the 2D landmark visibilities via surface normals.
We gather a substantially larger collection of all-pose face images to evaluate
our algorithm and demonstrate superior performances than the state-of-the-art
methods.

本文提出一种新的面部对齐算法，利用 3D 可变形模型和级联耦合回归器，估计带有任意姿态的人脸图像的 2D 和 3D 地标及其 2D 可视性。实验结果表明，该算法在所有姿态人脸图像上的表现优于现有方法。