  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.
