3D face reconstruction from single image with generative adversarial networks

Traditional reconstruction techniques extract information from the object's geometry or one or more 2D images. On the other hand, the limit of the existing methods is that they generate less precise objects. Thus the lack of robustness towards several face reconstruction problems, such as the position of the head, occlusion, noise, and lighting variation. Therefore, generative neural networks and graphical convolution networks have marked a significant evolution in the field of 3D reconstruction. This paper proposes a model for 3D face reconstruction from a single 2D image. Our model is composed of a generator and a discriminator based on convolutional graphic layers. Indeed, in order to generate a face mesh with expression, our idea is to use the landmarks associated with this image as input to the generator to reconstruct a face geometry with expression and improve the convergence rate. As a result, our model offers an accurate reconstruction of facial geometry with expression; thus, our model outperforms state-of-the-art methods through qualitative and quantitative comparison.