Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency.However, training deep neural networks typically requires a large volume of data, whereas face images with ground-truth 3D face shapes are scarce. In this paper, we propose a novel deep 3D face reconstruction approach that 1) leverages a robust, hybrid loss function for weakly-supervised learning which takes into account both low-level and perception-level information for supervision, and 2) performs multi-image face reconstruction by exploiting complementary information from different images for shape aggregation. Our method is fast, accurate, and robust to occlusion and large pose. We provide comprehensive experiments on three datasets, systematically comparing our method with fifteen recent methods and demonstrating its state-of-the-art performance.

本文提出了一种使用深度学习技术进行3D面部重建的新方法，其中使用了混合损失函数进行弱监督学习，同时利用多个图像进行面部重建，实现了快速、准确、鲁棒的重建效果，对不同数据集进行了综合实验，并与15种现有方法进行了比较，证明了其性能处于领先地位。

弱监督学习下的精确三维人脸重构：从单张图像到图像集