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In this paper, we propose a novel multi-level aggregation network to regressthe coordinates of the vertices of a 3D face from a single 2D image in anend-to-end manner. This is achieved by seamlessly combining standardconvolutional neural networks (CNNs) with Graph Convolution Networks (GCNs). Byiteratively and hierarchically fusing the features across different layers andstages of the CNNs and GCNs, our approach can provide a dense face alignmentand 3D face reconstruction simultaneously for the benefit of direct featurelearning of 3D face mesh. Experiments on several challenging datasetsdemonstrate that our method outperforms state-of-the-art approaches on both 2Dand 3D face alignment tasks.

3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs