Embedding 3D morphable basis functions into deep neural networks opens great
potential for models with better representation power. However, to faithfully
learn those models from an image collection, it requires strong regularization
to overcome ambiguities involved in the learning process. This critically
prevents us from learning high fidelity face models which are needed to
represent face images in high level of details. To address this problem, this
paper presents a novel approach to learn additional proxies as means to
side-step strong regularizations, as well as, leverages to promote detailed
shape/albedo. To ease the learning, we also propose to use a dual-pathway
network, a carefully-designed architecture that brings a balance between global
and local-based models. By improving the nonlinear 3D morphable model in both
learning objective and network architecture, we present a model which is
superior in capturing higher level of details than the linear or its precedent
nonlinear counterparts. As a result, our model achieves state-of-the-art
performance on 3D face reconstruction by solely optimizing latent
representations.

该论文提出了一种通过学习额外的代理来学习高保真人脸模型的新方法，结合全局和局部模型的精心设计网络，通过改进非线性 3D 可变形模型的学习目标和网络架构，使其在捕捉更高级别细节方面具有优越性，从而在 3D 人脸重建中实现了最先进的表现。