Can I read Transformer-Based 3D Face Reconstruction With End-to-End Shape-Preserved Domain Transfer on EtoBox?

Transformer-Based 3D Face Reconstruction With End-to-End Shape-Preserved Domain Transfer by Zhuo Chen; Yuesong Wang; Tao Guan; Luoyuan Xu; Wenkai Liu is a Computer Science article available to read on EtoBox.

What is Transformer-Based 3D Face Reconstruction With End-to-End Shape-Preserved Domain Transfer about?

Learning-based face reconstruction methods have recently shown promising performance in recovering face geometry from a single image. However, the lack of training data with 3D annotations severely limits the performance. To tackle this problem, we proposed a novel end-to-end 3D face reconstruction network consisting of a conditional GAN (cGAN) for cross-domain face synthesis and a novel mesh transformer for face reconstruction. Our method first uses cGAN to translate the realistic face images to the specific rendered style, with a 2D facial edge consistency loss function. The domain-transferred images are then fed into face reconstruction network which uses a novel mesh transformer to output 3D mesh vertices. To exploit the domain-transferred in-the-wild images, we further propose a reprojection consistency loss to restrict face reconstruction network in a self-supervised way. Our approach can be trained with annotated dataset, synthetic dataset and in-the-wild images to learn a unified face model. Extensive experiments have demonstrated the effectiveness of our method.

Who reads Transformer-Based 3D Face Reconstruction With End-to-End Shape-Preserved Domain Transfer?

Author

Zhuo Chen; Yuesong Wang; Tao Guan; Luoyuan Xu; Wenkai Liu

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)