[CVPR2020] Adversarial Latent Autoencoders

papers about Face Detection; Face Alignment; Face Recognition && Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution && Face Deblurring; Face Generation && Face Synthesis; Face Transfer; Face Anti-Spoofing; Face Retrieval;

A large-scale face dataset for face parsing, recognition, generation and editing.

A face recognition solution on mobile device.

TF implementation of our ECCV 2018 paper: Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model

[ECCV 2018] ReenactGAN: Learning to Reenact Faces via Boundary Transfer

This is the accompanying code for the Medium article: https://medium.com/@karol_majek/high-resolution-face2face-with-pix2pix-1024x1024-37b90c1ca7e8

Notebook for O'Reilly's "Deep Convolutional Generative Adversarial Networks"

Caffe/C++ implementation of Boundary Equilibrium Generative Adversarial Networks paper for face generation

Deep Convolutional Conditional GAN and Supervised CNN for generating children's faces given parents' faces

Various implementations and projects on CNN, RNN, LSTM, GAN, etc

Udacity nanodegree projects: DLND, DRLND, DAND

Simple deepfake pet-project

Generate new faces using GAN made in PyTorch.

Generate Random Faces which do not exists. I trained a Deep Convolution GAN (DCGAN) on 100k celebrities photos.

Uses generative adversarial networks to create images of faces

face generating DCGAN implemented by Keras

Controllable Face Generation via pretrained Conditional Adversarial Latent Autoencoder (ALAE)

Using GAN to generate human faces (Tensorflow).

在该项目中，你将使用生成式对抗网络（Generative Adversarial Nets）来生成新的人脸图像。

Contains my experiments with GANs.

This program generates realistic human faces using a neural network architecture known as a variational autoencoder. Written using the Keras API in the Tensorflow library. Weights are included.

VoiceGAN - Hallucinating Faces from Voices

using GANs generate face.

Keras implementation of Variation Autoencoder for face generation. Analysis of the distribution of the latent space of the VAE. Vector arithemtic in the latent space. Morphing between the faces. The model was trained on CelebA dataset

Implementations of various GANs using CelebA Dataset and PyTorch

Implementation of DCGAN in Tensorflow and Torch7

Basel morphable face model mesh and texture generator using GPU.

This is the project submitted as a part of Facebook PyTorch challenge scholarship Deep Learning Nanodegree program. The original repo of Udacity can be found at https://github.com/udacity/deep-learning-v2-pytorch

Defined and trained a DCGAN on a dataset of faces. The Goal of this project is to generate new images of faces that look as realistic as possible.