3D Dense Face Alignment via Graph Convolution Networks
This work addresses 3D face reconstruction and alignment for computer vision applications, representing an incremental improvement.
The paper tackles 3D dense face alignment by proposing a graph convolution network to regress 3D face coordinates, achieving superior performance over state-of-the-art methods on challenging datasets.
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.