While current monocular 3D face reconstruction methods can recover fine
geometric details, they suffer several limitations. Some methods produce faces
that cannot be realistically animated because they do not model how wrinkles
vary with expression. Other methods are trained on high-quality face scans and
do not generalize well to in-the-wild images. We present the first approach
that regresses 3D face shape and animatable details that are specific to an
individual but change with expression. Our model, DECA (Detailed Expression
Capture and Animation), is trained to robustly produce a UV displacement map
from a low-dimensional latent representation that consists of person-specific
detail parameters and generic expression parameters, while a regressor is
trained to predict detail, shape, albedo, expression, pose and illumination
parameters from a single image. To enable this, we introduce a novel
detail-consistency loss that disentangles person-specific details from
expression-dependent wrinkles. This disentanglement allows us to synthesize
realistic person-specific wrinkles by controlling expression parameters while
keeping person-specific details unchanged. DECA is learned from in-the-wild
images with no paired 3D supervision and achieves state-of-the-art shape
reconstruction accuracy on two benchmarks. Qualitative results on in-the-wild
data demonstrate DECA's robustness and its ability to disentangle identity- and
expression-dependent details enabling animation of reconstructed faces. The
model and code are publicly available at this https URL

本文提出了一种名为 DECA 的模型，可以从单张图片中预测 3D 面部的个性细节、形状、色调、表情、姿势和光照，并通过一种新颖的细节一致性损失实现了个性的详情与表情的皱纹的解耦，从而使得重建面部的动画更加逼真。该模型能够在无 3D 监督的情况下进行学习，并在两个基准测试上达到最先进的形状重建精度。