Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild

Demo | Project Page | Video | Paper

Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi, Visual Geometry Group, University of Oxford. In CVPR 2020 (oral).

We propose a method to learn weakly symmetric deformable 3D object categories from raw single-view images, without ground-truth 3D, multiple views, 2D/3D keypoints, prior shape models or any other supervision.

Setup (with Anaconda)

1. Install dependencies:

conda env create -f environment.yml

OR manually:

conda install -c conda-forge scikit-image matplotlib opencv moviepy pyyaml tensorboardX

2. Install PyTorch:

conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=9.2 -c pytorch

Note: The code is tested with PyTorch 1.2.0 and CUDA 9.2 on CentOS 7. A GPU version is required for training and testing, since the neural_renderer package only has GPU implementation. You are still able to run the demo without GPU.

3. Install neural_renderer:

This package is required for training and testing, and optional for the demo. It requires a GPU device and GPU-enabled PyTorch.

pip install neural_renderer_pytorch

Note: It may fail if you have a GCC version below 5. If you do not want to upgrade your GCC, one alternative solution is to use conda's GCC and compile the package from source. For example:

conda install gxx_linux-64=7.3
git clone https://github.com/daniilidis-group/neural_renderer.git
cd neural_renderer
python setup.py install

4. (For demo only) Install facenet-pytorch:

This package is optional for the demo. It allows automatic human face detection.

pip install facenet-pytorch

Datasets

CelebA face dataset. Please download the original images (img_celeba.7z) from their website and run celeba_crop.py in data/ to crop the images.
Synthetic face dataset generated using Basel Face Model. This can be downloaded using the script download_synface.sh provided in data/.
Cat face dataset composed of Cat Head Dataset and Oxford-IIIT Pet Dataset (license). This can be downloaded using the script download_cat.sh provided in data/.

Please remember to cite the corresponding papers if you use these datasets.

Pretrained Models

Download pretrained models using the scripts provided in pretrained/, eg:

cd pretrained && sh download_pretrained_celeba.sh

Demo

python -m demo.demo --input demo/images/human_face --result demo/results/human_face --checkpoint pretrained/pretrained_celeba/checkpoint030.pth

Options:

--gpu: enable GPU
--detect_human_face: enable automatic human face detection and cropping using MTCNN provided in facenet-pytorch. This does not work on cat images. You will need to manually crop the images instead.
--render_video: render 3D animations using neural_renderer (GPU is required)

Training and Testing

Check the configuration files in experiments/ and run experiments, eg:

python run.py --config experiments/train_celeba.yml --gpu 0 --num_workers 4

Citation

@InProceedings{Wu_2020_CVPR,
  author = {Shangzhe Wu and Christian Rupprecht and Andrea Vedaldi},
  title = {Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild},
  booktitle = {CVPR},
  year = {2020}
}

johnbhlm/unsup3d