Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

The Generative Models have gained considerable attention in unsupervised learning via a
new and practical framework called Generative Adversarial Networks (GAN) due to their …

A good image-to-image translation model should learn a map** between different visual
domains while satisfying the following properties: 1) diversity of generated images and 2) …

We present a general framework for exemplar-based image translation, which synthesizes a
photo-realistic image from the input in a distinct domain (eg, semantic segmentation mask …

Image-to-image translation (I2I) aims to transfer images from a source domain to a target
domain while preserving the content representations. I2I has drawn increasing attention and …

We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a
clothed human and turns them into an animatable avatar. These avatars are driven by pose …

Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

While deep face recognition (FR) systems have shown amazing performance in
identification and verification, they also arouse privacy concerns for their excessive …

Generative adversarial networks (GANs) synthesize realistic images from random latent
vectors. Although manipulating the latent vectors controls the synthesized outputs, editing …

Facial attribute editing aims to manipulate single or multiple attributes on a given face
image, ie, to generate a new face image with desired attributes while preserving other …