face-generation

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;

[CVPR2020] Adversarial Latent Autoencoders

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

[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

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

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

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

Udacity nanodegree projects: DLND, DRLND, DAND

Implementations of various GANs using CelebA Dataset and PyTorch

Experimental repository attempting to project facial landmarks into the StyleGAN2 latent space.

Simple deepfake pet-project

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

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

使用ONNXRuntime部署U-2-Net生成人脸素描画，包含C++和Python两个版本的程序

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

Generate new faces using GAN made in PyTorch.

Interpretable Control Exploration and Counterfactual Explanation (ICE) on StyleGAN

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

GAN-based generation of animated/ACGN faces. || 应用训练 GAN，数据集来源于李宏毅老师的二次元人脸/头像

face generating DCGAN implemented by Keras

Text-based dataset with comprehensive facial expression sentence

Uses generative adversarial networks to create images of faces

Using GAN to generate human faces (Tensorflow).

VoiceGAN - Hallucinating Faces from Voices