3D face reconstruction from a single 2D image is a challenging problem with
broad applications. Recent methods typically aim to learn a CNN-based 3D face
model that regresses coefficients of 3D Morphable Model (3DMM) from 2D images
to render 3D face reconstruction or dense face alignment. However, the shortage
of training data with 3D annotations considerably limits performance of those
methods. To alleviate this issue, we propose a novel 2D-assisted
self-supervised learning (2DASL) method that can effectively use "in-the-wild"
2D face images with noisy landmark information to substantially improve 3D face
model learning. Specifically, taking the sparse 2D facial landmarks as
additional information, 2DSAL introduces four novel self-supervision schemes
that view the 2D landmark and 3D landmark prediction as a self-mapping process,
including the 2D and 3D landmark self-prediction consistency, cycle-consistency
over the 2D landmark prediction and self-critic over the predicted 3DMM
coefficients based on landmark predictions. Using these four self-supervision
schemes, the 2DASL method significantly relieves demands on the the
conventional paired 2D-to-3D annotations and gives much higher-quality 3D face
models without requiring any additional 3D annotations. Experiments on multiple
challenging datasets show that our method outperforms state-of-the-arts for
both 3D face reconstruction and dense face alignment by a large margin.

使用 2D 图像辅助自监督学习的方法可大幅提高 3D 人脸模型的学习效果，该方法引入 4 种自监督方案，显著降低了对传统 2D 到 3D 配对注释的需求，从而在多种数据集上均取得大幅优于现有技术的 3D 重建和面部对齐结果。