We present a self-supervised learning approach to learning monocular 3D face
reconstruction with a pose guidance network (PGN). First, we unveil the
bottleneck of pose estimation in prior parametric 3D face learning methods, and
propose to utilize 3D face landmarks for estimating pose parameters. With our
specially designed PGN, our model can learn from both faces with fully labeled
3D landmarks and unlimited unlabeled in-the-wild face images. Our network is
further augmented with a self-supervised learning scheme, which exploits face
geometry information embedded in multiple frames of the same person, to
alleviate the ill-posed nature of regressing 3D face geometry from a single
image. These three insights yield a single approach that combines the
complementary strengths of parametric model learning and data-driven learning
techniques. We conduct a rigorous evaluation on the challenging AFLW2000-3D,
Florence and FaceWarehouse datasets, and show that our method outperforms the
state-of-the-art for all metrics.

本研究提出自监督学习的方法，使用姿势引导网络 (PGN) 进行单眼 3D 人脸重建，并通过结合参数模型的学习和数据驱动的学习技术，从完全标记的 3D 地标和无限制的野外面部图像中学习。通过使用包含同一人物多帧嵌入的面部几何信息的自监督学习方案，缓解了从单个图像回归 3D 面部几何的不适定性。在 AFLW2000-3D、Florence 和 FaceWarehouse 数据集上进行了严格评估，并表明我们的方法在所有指标上均优于最先进技术。