face-generation

[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

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

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

[CVPR 2021] A large-scale face image dataset that allows text-to-image-generation, text-guided image manipulation, sketch-to-image generation, GANs for face generation and editing, image caption, and VQA.

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

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

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

Udacity nanodegree projects: DLND, DRLND, DAND

Simple deepfake pet-project

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

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

👨‍🦰 Large Scale Active Social Engineering Defense (ASED): Multimedia and Social Engineering

Generate new faces using GAN made in PyTorch.

Keras implementation of WGAN GP for face generation. The model is trained on CelebA dataset.

face generating DCGAN implemented by Keras

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

VoiceGAN - Hallucinating Faces from Voices

Uses generative adversarial networks to create images of faces

Text-based dataset with comprehensive facial expression sentence

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.

Basel morphable face model mesh and texture generator using GPU.