We present a method for training a regression network from image pixels to 3D
morphable model coordinates using only unlabeled photographs. The training loss
is based on features from a facial recognition network, computed on-the-fly by
rendering the predicted faces with a differentiable renderer. To make training
from features feasible and avoid network fooling effects, we introduce three
objectives: a batch distribution loss that encourages the output distribution
to match the distribution of the morphable model, a loopback loss that ensures
the network can correctly reinterpret its own output, and a multi-view identity
loss that compares the features of the predicted 3D face and the input
photograph from multiple viewing angles. We train a regression network using
these objectives, a set of unlabeled photographs, and the morphable model
itself, and demonstrate state-of-the-art results.

本文提出了一种基于人脸图像的无监督学习方法，使用面部识别网络特征从像素到 3D 型可变模型坐标的回归网络训练，通过可微分的渲染器进行预测脸部的渲染并计算特征，引入批量分布损失、循环损失和多视图身份损失来实现从特征学习并避免网络欺骗效应，在使用未标记的照片和可变形模型进行训练，展示了最先进的结果。