Recent 3D face reconstruction methods have made significant progress in shape
estimation, but high-fidelity facial albedo reconstruction remains challenging.
Existing methods depend on expensive light-stage captured data to learn facial
albedo maps. However, a lack of diversity in subjects limits their ability to
recover high-fidelity results. In this paper, we present a novel facial albedo
reconstruction model, HiFiAlbedo, which recovers the albedo map directly from a
single image without the need for captured albedo data. Our key insight is that
the albedo map is the illumination invariant texture map, which enables us to
use inexpensive texture data to derive an albedo estimation by eliminating
illumination. To achieve this, we first collect large-scale
ultra-high-resolution facial images and train a high-fidelity facial texture
codebook. By using the FFHQ dataset and limited UV textures, we then fine-tune
the encoder for texture reconstruction from the input image with adversarial
supervision in both image and UV space. Finally, we train a cross-attention
module and utilize group identity loss to learn the adaptation from facial
texture to the albedo domain. Extensive experimentation has demonstrated that
our method exhibits excellent generalizability and is capable of achieving
high-fidelity results for in-the-wild facial albedo recovery. Our code,
pre-trained weights, and training data will be made publicly available at
this https URL

本文介绍了一个新颖的面部 albedo 重建模型 HiFiAlbedo，通过使用廉价的纹理数据消除光照来直接从单个图像中恢复 albedo 图，并通过训练高保真面部纹理代码簿、微调纹理重建编码器和使用组标识损失学习纹理到 albedo 领域的自适应来实现高保真结果的实验验证。

基于纹理量化的高保真人脸反射率估计

High-Fidelity Facial Albedo Estimation via Texture Quantization

3D Morphable Model (3DMM) based methods have achieved great success in
recovering 3D face shapes from single-view images. However, the facial textures
recovered by such methods lack the fidelity as exhibited in the input images.
Recent work demonstrates high-quality facial texture recovering with generative
networks trained from a large-scale database of high-resolution UV maps of face
textures, which is hard to prepare and not publicly available. In this paper,
we introduce a method to reconstruct 3D facial shapes with high-fidelity
textures from single-view images in-the-wild, without the need to capture a
large-scale face texture database. The main idea is to refine the initial
texture generated by a 3DMM based method with facial details from the input
image. To this end, we propose to use graph convolutional networks to
reconstruct the detailed colors for the mesh vertices instead of reconstructing
the UV map. Experiments show that our method can generate high-quality results
and outperforms state-of-the-art methods in both qualitative and quantitative
comparisons.

本文提出了一种利用图卷积网络从单视角图像中重建具有高保真度贴图的三维人脸形状的方法，并相对于其他的现有方法具有更好的定量和定性效果。

使用图卷积网络实现从野外图像进行高保真度 3D 人脸重建

Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images  Using Graph Convolutional Networks

We present a method for synthesizing a frontal, neutral-expression image of a
person's face given an input face photograph. This is achieved by learning to
generate facial landmarks and textures from features extracted from a
facial-recognition network. Unlike previous approaches, our encoding feature
vector is largely invariant to lighting, pose, and facial expression.
Exploiting this invariance, we train our decoder network using only frontal,
neutral-expression photographs. Since these photographs are well aligned, we
can decompose them into a sparse set of landmark points and aligned texture
maps. The decoder then predicts landmarks and textures independently and
combines them using a differentiable image warping operation. The resulting
images can be used for a number of applications, such as analyzing facial
attributes, exposure and white balance adjustment, or creating a 3-D avatar.

该论文提出了一种生成正面中性面部图像的方法，通过从面部识别网络提取的特征来生成面部标记和纹理。通过不变性编码特征向量，使用只包含正面中性照片来训练解码器网络，解码器独立预测标记和纹理，然后使用可微分图像变形操作将它们组合在一起，最终得到可以用于多种应用的合成图像。