In this paper, we present a large-scale detailed 3D face dataset, FaceScape,
and the corresponding benchmark to evaluate single-view facial 3D
reconstruction. By training on FaceScape data, a novel algorithm is proposed to
predict elaborate riggable 3D face models from a single image input. FaceScape
dataset provides 18,760 textured 3D faces, captured from 938 subjects and each
with 20 specific expressions. The 3D models contain the pore-level facial
geometry that is also processed to be topologically uniformed. These fine 3D
facial models can be represented as a 3D morphable model for rough shapes and
displacement maps for detailed geometry. Taking advantage of the large-scale
and high-accuracy dataset, a novel algorithm is further proposed to learn the
expression-specific dynamic details using a deep neural network. The learned
relationship serves as the foundation of our 3D face prediction system from a
single image input. Different than the previous methods, our predicted 3D
models are riggable with highly detailed geometry under different expressions.
We also use FaceScape data to generate the in-the-wild and in-the-lab benchmark
to evaluate recent methods of single-view face reconstruction. The accuracy is
reported and analyzed on the dimensions of camera pose and focal length, which
provides a faithful and comprehensive evaluation and reveals new challenges.
The unprecedented dataset, benchmark, and code have been released to the public
for research purpose.

本文提出了一个大规模的详细 3D 人脸数据集（FaceScape）及相应的评估标准来评估单视图面部三维重建，通过该数据集，提出了一种从单张图像输入预测精细可调和三维人脸模型的新算法，并且使用该数据集来生成用于评估单视图面部重建方法的基准数据，并报告和分析了在不同条件下的准确性和挑战。

FaceScape：单视角 3D 人脸重建的三维面部数据集和基准

FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face  Reconstruction

In this paper, we present a large-scale detailed 3D face dataset, FaceScape,
and propose a novel algorithm that is able to predict elaborate riggable 3D
face models from a single image input. FaceScape dataset provides 18,760
textured 3D faces, captured from 938 subjects and each with 20 specific
expressions. The 3D models contain the pore-level facial geometry that is also
processed to be topologically uniformed. These fine 3D facial models can be
represented as a 3D morphable model for rough shapes and displacement maps for
detailed geometry. Taking advantage of the large-scale and high-accuracy
dataset, a novel algorithm is further proposed to learn the expression-specific
dynamic details using a deep neural network. The learned relationship serves as
the foundation of our 3D face prediction system from a single image input.
Different than the previous methods, our predicted 3D models are riggable with
highly detailed geometry under different expressions. The unprecedented dataset
and code will be released to public for research purpose.

本文介绍了一个大规模的详细的 3D 人脸数据集 FaceScape，并提出了一种新算法，可以从单个图像输入预测精细的可控 3D 面部模型。该数据集提供了来自 938 个受试者的 18,760 个纹理 3D 面部，每个面部有 20 个具体表情。这些细致的面部模型可以表示为 3D 可变模型和位移图，利用大规模和高精度数据集，进一步提出了一种学习特定表情动态细节的新型算法。与之前的方法不同，我们预测的 3D 模型在不同表情下可以高度自由控制，数据集和代码将公开发布供研究使用。