论文标题
通过聚合CNN和GCN的融合特征的3D密集面对齐
3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs
论文作者
论文摘要
在本文中，我们提出了一个新型的多级聚合网络，以端到端的方式从单个2D图像中回归3D面的顶点的坐标。这是通过将标准卷积神经网络（CNN）与图形卷积网络（GCN）无缝相结合的。通过迭代和分层融合CNN和GCN的不同层和阶段的特征，我们的方法可以同时提供密集的面部对齐和3D脸重建，以使3D Face Mesh的直接特征学习受益。在几个具有挑战性的数据集上进行的实验表明，我们的方法在2D和3D面对对齐任务上都优于最先进的方法。
In this paper, we propose a novel multi-level aggregation network to regress the coordinates of the vertices of a 3D face from a single 2D image in an end-to-end manner. This is achieved by seamlessly combining standard convolutional neural networks (CNNs) with Graph Convolution Networks (GCNs). By iteratively and hierarchically fusing the features across different layers and stages of the CNNs and GCNs, our approach can provide a dense face alignment and 3D face reconstruction simultaneously for the benefit of direct feature learning of 3D face mesh. Experiments on several challenging datasets demonstrate that our method outperforms state-of-the-art approaches on both 2D and 3D face alignment tasks.
