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.
翻译:我们展示了一种自我监督的学习方法来学习单立方体面部的重建。 首先,我们揭开了先前3D面部模拟学习方法中面部估计的瓶颈,并提议利用3D面部标志来估计面部参数。我们专门设计的PGN,我们的模型可以用完全贴上标签的3D标志和无限制无标签的面部图像从两张脸部中学习。我们的网络通过一个自我监督的学习计划得到进一步加强,该计划利用了嵌入同一人多个框架的面部几何学信息,以缓解从一个图像中反向3D面部面部几何学的错误性质。这三个洞见产生了一种单一的方法,将参数模型学习和数据驱动的学习技术的互补优势结合起来。我们对具有挑战性的AFLW2000-3D、Florence和FaceWarehouse数据集进行了严格的评估,并表明我们的方法超越了所有指标的状态。