We present a data-driven inference method that can synthesize a
photorealistic texture map of a complete 3D face model given a partial 2D view
of a person in the wild. After an initial estimation of shape and low-frequency
albedo, we compute a high-frequency partial texture map, without the shading
component, of the visible face area. To extract the fine appearance details
from this incomplete input, we introduce a multi-scale detail analysis
technique based on mid-layer feature correlations extracted from a deep
convolutional neural network. We demonstrate that fitting a convex combination
of feature correlations from a high-resolution face database can yield a
semantically plausible facial detail description of the entire face. A complete
and photorealistic texture map can then be synthesized by iteratively
optimizing for the reconstructed feature correlations. Using these
high-resolution textures and a commercial rendering framework, we can produce
high-fidelity 3D renderings that are visually comparable to those obtained with
state-of-the-art multi-view face capture systems. We demonstrate successful
face reconstructions from a wide range of low resolution input images,
including those of historical figures. In addition to extensive evaluations, we
validate the realism of our results using a crowdsourced user study.

利用深度卷积神经网络提取中层特征，从高分辨率人脸数据库中拟合特征的凸组合，从而生成 photorealistic texture map，以实现从低分辨率输入图像中合成高逼真度的 3D 人脸渲染模型，并进行了广泛的验证。