cientgu / Mask_guided_portrait_editing
pytorch implementation of "Mask-Guided Portrait Editing with Conditional GANs"
Stars: ✭ 155
Programming Languages
python
139335 projects - #7 most used programming language
Labels
Projects that are alternatives of or similar to Mask guided portrait editing
Focal Frequency Loss
Focal Frequency Loss for Generative Models
Stars: ✭ 141 (-9.03%)
Mutual labels: gan
Tensorflow Pix2pix
A lightweight pix2pix Tensorflow implementation.
Stars: ✭ 143 (-7.74%)
Mutual labels: gan
Pix2pixbegan.pytorch
A pytorch implementation of pix2pix + BEGAN (Boundary Equilibrium Generative Adversarial Networks)
Stars: ✭ 148 (-4.52%)
Mutual labels: gan
Data science blogs
A repository to keep track of all the code that I end up writing for my blog posts.
Stars: ✭ 139 (-10.32%)
Mutual labels: gan
Pix2latent
Code for: Transforming and Projecting Images into Class-conditional Generative Networks
Stars: ✭ 141 (-9.03%)
Mutual labels: gan
Nice Gan Pytorch
Official PyTorch implementation of NICE-GAN: Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation
Stars: ✭ 140 (-9.68%)
Mutual labels: gan
Pytorch Gan
A minimal implementaion (less than 150 lines of code with visualization) of DCGAN/WGAN in PyTorch with jupyter notebooks
Stars: ✭ 150 (-3.23%)
Mutual labels: gan
Person Reid Gan Pytorch
A Pytorch Implementation of "Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro"(ICCV17)
Stars: ✭ 147 (-5.16%)
Mutual labels: gan
Glcic Pytorch
A High-Quality PyTorch Implementation of "Globally and Locally Consistent Image Completion".
Stars: ✭ 141 (-9.03%)
Mutual labels: gan
Lr Gan.pytorch
Pytorch code for our ICLR 2017 paper "Layered-Recursive GAN for image generation"
Stars: ✭ 145 (-6.45%)
Mutual labels: gan
Tsit
[ECCV 2020 Spotlight] A Simple and Versatile Framework for Image-to-Image Translation
Stars: ✭ 141 (-9.03%)
Mutual labels: gan
Tensorflow Infogan
🎎 InfoGAN: Interpretable Representation Learning
Stars: ✭ 149 (-3.87%)
Mutual labels: gan
Art Dcgan
Modified implementation of DCGAN focused on generative art. Includes pre-trained models for landscapes, nude-portraits, and others.
Stars: ✭ 1,882 (+1114.19%)
Mutual labels: gan
Shapegan
Generative Adversarial Networks and Autoencoders for 3D Shapes
Stars: ✭ 151 (-2.58%)
Mutual labels: gan
Stylegan.pytorch
A PyTorch implementation for StyleGAN with full features.
Stars: ✭ 150 (-3.23%)
Mutual labels: gan
Mask-Guided Portrait Editing with Conditional GANs
This is an official pytorch implementation of "Mask-Guided Portrait Editing with Conditional GANs"(CVPR2019). The major contributors of this repository include Shuyang Gu, Jianmin Bao, Hao Yang, Dong Chen, Fang Wen, Lu Yuan at Microsoft Research.
Introduction
Mask-Guided Portrait Editing is a novel technology based on mask-guided condititonal GANs, which can synthesize diverse, high-quality and controllable facial images from given masks. With the changeable input facial mask and source image, this method allows users to do high-level portrait editing.
Citation
If you find our code helpful for your research, please consider citing:
@inproceedings{gu2019mask,
title={Mask-Guided Portrait Editing With Conditional GANs},
author={Gu, Shuyang and Bao, Jianmin and Yang, Hao and Chen, Dong and Wen, Fang and Yuan, Lu},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={3436--3445},
year={2019}
}
Getting Started
Prerequisite
- Linux.
- Pytorch 0.4.1.
- Nvidia GPU: K40, M40, P100.
- CUDA9.2 or 10.
Running code
- download pretrained models here, put it under folder checkpoints/pretrained .
- component editing: ./scripts/test_edit.sh
- component transfer: ./scripts/test_edit_free_encode.sh change the corresponding component file in results/pretrained/editfree_latest, then run: ./scripts/test_edit_free_generate.sh get the component transfer results.
- training: ./scripts/train.sh
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].