This paper proposes an encoder-decoder network to disentangle shape features
during 3D face reconstruction from single 2D images, such that the tasks of
reconstructing accurate 3D face shapes and learning discriminative shape
features for face recognition can be accomplished simultaneously. Unlike
existing 3D face reconstruction methods, our proposed method directly regresses
dense 3D face shapes from single 2D images, and tackles identity and residual
(i.e., non-identity) components in 3D face shapes explicitly and separately
based on a composite 3D face shape model with latent representations. We devise
a training process for the proposed network with a joint loss measuring both
face identification error and 3D face shape reconstruction error. To construct
training data we develop a method for fitting 3D morphable model (3DMM) to
multiple 2D images of a subject. Comprehensive experiments have been done on
MICC, BU3DFE, LFW and YTF databases. The results show that our method expands
the capacity of 3DMM for capturing discriminative shape features and facial
detail, and thus outperforms existing methods both in 3D face reconstruction
accuracy and in face recognition accuracy.

本文提出了一种编码器 - 解码器网络架构，用于从单个 2D 图像中分离出形状特征，从而能够同时实现重建准确的 3D 人脸形状和学习面部识别的判别形状特征。与现有的 3D 人脸重建方法不同，我们的方法直接从单个 2D 图像回归密集的 3D 人脸形状，并根据一个含有潜在表示的组合 3D 人脸形状模型，明确地分开处理了 3D 人脸形状的身份和残差（即非身份）部分。我们制定了一个训练流程，用联合损失来衡量面部识别错误和 3D 人脸形状重建错误，为此我们开发了一种将 3D 形态可塑模型（3DMM）适配到多幅同一主体的 2D 图像上的方法。广泛的实验在 MICC、BU3DFE、LFW 和 YTF 数据库上进行。结果表明，我们的方法扩展了 3DMM 捕捉判别形状特征和面部细节的能力，因此在 3D 人脸重建准确性和面部识别准确性方面均优于现有方法。