  Recently, 3D face reconstruction and face alignment tasks are gradually
combined into one task: 3D dense face alignment. Its goal is to reconstruct the
3D geometric structure of face with pose information. In this paper, we propose
a graph convolution network to regress 3D face coordinates. Our method directly
performs feature learning on the 3D face mesh, where the geometric structure
and details are well preserved. Extensive experiments show that our approach
gains superior performance over state-of-the-art methods on several challenging
datasets.
