‘AvatarMe’- creates 3D avatars of people from nothing but face images

Researchers of Imperial College London and FaceSoft.io, a startup working on machine learning is working to build AvatarMe.

‘AvatarMe’- creates 3D avatars of people from nothing but face images

Facesoft is an AI startup based in London. It combines cutting edge machine learning and computer vision technology for 2D/3D face analysis applications.

Detailed paper url : https://arxiv.org/pdf/2003.13845.pdf

Generative Adversarial Network (GAN)

A generative adversarial network, or GAN, is a deep neural network framework which is able to learn from a set of training data and generate new data with the same characteristics as the training data.

A generative adversarial network (GAN) is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. Two neural networks contest with each other in a game (in the form of a zero-sum game, where one agent’s gain is another agent’s loss).

Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning, GANs have also proven useful for semi-supervised learning, fully supervised learning, and reinforcement learning.

For example, a generative adversarial network trained on photographs of human faces can generate realistic-looking faces which are entirely fictitious.

Generative adversarial networks consist of two neural networks, the generator and the discriminator.