Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/xingangpan/gan2shape
Code for GAN2Shape (ICLR2021 oral)
https://github.com/xingangpan/gan2shape
3d-reconstruction deep-learning gan generative-adversarial-network inverse-graphics
Last synced: 6 days ago
JSON representation
Code for GAN2Shape (ICLR2021 oral)
- Host: GitHub
- URL: https://github.com/xingangpan/gan2shape
- Owner: XingangPan
- License: mit
- Created: 2020-11-02T09:54:43.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2023-07-04T03:56:43.000Z (over 1 year ago)
- Last Synced: 2024-10-14T12:11:25.959Z (3 months ago)
- Topics: 3d-reconstruction, deep-learning, gan, generative-adversarial-network, inverse-graphics
- Language: Python
- Homepage: https://arxiv.org/abs/2011.00844
- Size: 21.8 MB
- Stars: 573
- Watchers: 28
- Forks: 101
- Open Issues: 35
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs
**Figure:** *Recovered 3D shape and rotation&relighting effects using GAN2Shape.*
> **Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs**
> Xingang Pan, Bo Dai, Ziwei Liu, Chen Change Loy, Ping Luo
> *ICLR2021* (**Oral**)
[[Paper](https://openreview.net/pdf?id=FGqiDsBUKL0)]
[[Project Page](https://xingangpan.github.io/projects/GAN2Shape.html)]
In this repository, we present **GAN2Shape**, which reconstructs the 3D shape of an image using off-the-shelf 2D image GANs in an unsupervised manner.
Our method **does not rely on mannual annotations or external 3D models**, yet it achieves high-quality 3D reconstruction, object rotation, and relighting effects.
## Requirements
* python>=3.6
* [pytorch](https://pytorch.org/)=1.1 or 1.2
* [neural_renderer](https://github.com/daniilidis-group/neural_renderer)
```sh
pip install neural_renderer_pytorch # or follow the guidance at https://github.com/elliottwu/unsup3d
```
* [mmcv](https://github.com/open-mmlab/mmcv)
```sh
pip install mmcv
```
* other dependencies
```sh
conda install -c conda-forge scikit-image matplotlib opencv pyyaml tensorboardX
```
## Dataset and pre-trained weights
To download dataset and pre-trained weights, simply run:
```sh
sh scripts/download.sh
```
## Training
Before training, you may optionally compile StyleGAN2 operations, which would be faster:
```sh
cd gan2shape/stylegan/stylegan2-pytorch/op
python setup.py install
cd ../../../..
```
**Example1**: training on car images:
```sh
sh scripts/run_car.sh
```
This would run on 4 GPUs by default. You can view the results at `results/car/images` or Tensorboard.
**Example2**: training on Celeba images:
```sh
sh scripts/run_celeba.sh
```
This by default uses our provided pre-trained weights. You can also perform joint pre-training via:
```sh
sh scripts/run_celeba-pre.sh
```
**Example3**: evaluating on synface (BFM) dataset:
```sh
sh scripts/run_synface.sh
```
This by default uses our provided pre-trained weights. You can also perform joint pre-training via:
```sh
sh scripts/run_synface-pre.sh
```
If you want to train on new StyleGAN2 samples, simply run the following script to generate new samples:
```sh
sh scripts/run_sample.sh
```
**Note**:
\- For human and cat faces, we perform joint training before instance-specific training, which produces better results.
\- For car and church, the quality of StyleGAN2 samples vary a lot, thus our approach may not produce good result on every sample. The downloaded dataset contains examples of good samples.
## Acknowledgement
Part of the code is borrowed from [Unsup3d](https://github.com/elliottwu/unsup3d) and [StyleGAN2](https://github.com/rosinality/stylegan2-pytorch).
Colab demo reproduced by [ucalyptus](https://github.com/ucalyptus): [Link](https://colab.research.google.com/drive/124D_f0RIu7Bbwa1SFV6pmvmBrNkB8Ow_?usp=sharing)
## BibTeX
```bibtex
@inproceedings{pan2020gan2shape,
title = {Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs},
author = {Pan, Xingang and Dai, Bo and Liu, Ziwei and Loy, Chen Change and Luo, Ping},
booktitle = {International Conference on Learning Representations},
year = {2021}
}
```