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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 fordoi:10.1109/cvprw.2019.00038 dblp:conf/cvpr/DengYX0JT19 fatcat:xcfulxaqfjafjlwoasjlcxw5r4