SURF-GAN
SURF-GAN copied to clipboard
[ECCV 2022] Official Pytorch implementation of "Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis"
Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis
Project page | Paper
"Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis"
Jeong-gi Kwak, Yuanming Li, Dongsik Yoon, Donghyeon Kim, David Han, Hanseok Ko
ECCV 2022
This repository includes the official Pytorch implementation of SURF-GAN.
SURF-GAN
SURF-GAN, which is a NeRF-based 3D-aware GAN, can discover disentangled semantic attributes in an unsupervised manner.
(Tranined on 64x64 CelebA and rendered with 256x256)
Get started
-
Clone the repo.
git clone https://github.com/jgkwak95/SURF-GAN.git
cd SURF-GAN
-
Create virtual environment
conda create -n surfgan python=3.7.1
conda activate surfgan
conda install -c pytorch-lts pytorch torchvision
pip install --no-cache-dir -r requirements.txt
Train SURF-GAN
At first, look curriculum.py and specify dataset and training options.
# CelebA
python train_surf.py --output_dir your-exp-name
--curriculum CelebA_single
Pretrained model
Pretrained model will be uploaded.
Semantic attribute discovery
Let's traverse each dimension with discovered semantics:
python discover_semantics.py --experiment your-exp-name
--image_size 256
--ray_step_multiplier 2
--num_id 9
--traverse_range 3.0
--intermediate_points 9
--curriculum CelebA_single
The default ckpt file to traverse is the latest file (generator.pth). If you want to check specific cpkt, add this in your command line, for example,
--specific_ckpt 140000_64_generator.pth
Control pose
In addition, you can control only camera paramters:
python control_pose.py --experiment your-exp-name
--image_size 128
--ray_step_multiplier 2
--num_id 9
--intermediate_points 9
--mode yaw
--curriculum CelebA_single
Render video
-
Moving camera
Set the mode: yaw, pitch, fov, etc. You can also make your trajectory.
python render_video.py --experiment your-exp-name
--image_size 128
--ray_step_multiplier 2
--num_frames 100
--curriculum CelebA_single
--mode yaw
-
Moving camera with a specific semantic
Choose an attribute that you want to control LiDj.
python render_video_semantic.py --experiment your-exp-name
--image_size 128
--ray_step_multiplier 2
--num_frames 100
--traverse_range 3.0
--intermediate_points
--curriculum CelebA_single
--mode circle
--L 2
--D 4
3D-Controllable StyleGAN
Injecting the prior of SURF-GAN into StyleGAN for controllable generation.
Also, it is compatible with many StyleGAN-based methods.
Video
Pose control | + Style (Toonify) |
---|---|
It is capable of editing real images directly. (with HyperStyle)
Pose | +Illumination (using SURF-GAN samples) |
---|---|
+Hair color (using SURF-GAN samples) | +Smile(using InterFaceGAN) |
---|---|
Citation
@article{kwak2022injecting,
title={Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis},
author={Kwak, Jeong-gi and Li, Yuanming and Yoon, Dongsik and Kim, Donghyeon and Han, David and Ko, Hanseok},
journal={arXiv preprint arXiv:2207.10257},
year={2022}
}
Acknowledgments
- SURF-GAN is bulided upon the pi-GAN implementation and inspired by EigenGAN (EigenGAN-pytorch).
- We used pSp encoder and StyleGAN2-pytorch to build 3D-controllable StyleGAN. For editing (challenging) real images, we exploited e4e and HyperStyle with our 3D-controllable StyleGAN.