Recently, generative adversarial networks (GANs) have progressed enormously, which
makes them able to learn complex data distributions in particular faces. More and more …

In recent years, researchers have proposed many deep learning (DL) methods for various
tasks, and particularly face recognition (FR) made an enormous leap using these …

Abstract 3D-aware generative adversarial networks (GANs) synthesize high-fidelity and
multi-view-consistent facial images using only collections of single-view 2D imagery …

We demonstrate that it is possible to perform face-related computer vision in the wild using
synthetic data alone. The community has long enjoyed the benefits of synthesizing training …

Generating synthetic datasets for training face recognition models is challenging because
dataset generation entails more than creating high fidelity images. It involves generating …

Deep learning, in particular the deep convolutional neural networks, has received
increasing interests in face recognition recently, and a number of deep learning methods …

We propose an approach for face image generation of virtual people with disentangled,
precisely-controllable latent representations for identity of non-existing people, expression …

In the past few years, a lot of work has been done towards reconstructing the 3D facial
structure from single images by capitalizing on the power of Deep Convolutional Neural …

Although 2D generative models have made great progress in face image generation and
animation, they often suffer from undesirable artifacts such as 3D inconsistency when …

Although significant advances have been made in face recognition (FR), FR in
unconstrained environments remains challenging due to the domain gap between the semi …