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(CVPR'20 Oral) Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
| Date | Stars |
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| 2026-07-24 | 1190 |
| 2026-07-25 | 1190 |
| 2026-07-28 | 1190 |
| 2026-07-30 | 1190 |
| 2026-08-06 | 1190 |
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# Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild #### [Demo](http://www.robots.ox.ac.uk/~vgg/blog/unsupervised-learning-of-probably-symmetric-deformable-3d-objects-from-images-in-the-wild.html) | [Project Page](https://elliottwu.com/projects/unsup3d/) | [Video](https://www.youtube.com/watch?v=5rPJyrU-WE4) | [Paper](https://arxiv.org/abs/1911.11130) [Shangzhe Wu](https://elliottwu.com/), [Christian Rupprecht](https://chrirupp.github.io/), [Andrea Vedaldi](http://www.robots.ox.ac.uk/~vedaldi/), Visual Geometry Group, University of Oxford. In CVPR 2020 (Best Paper Award). <img src="./img/teaser.jpg" width="800"> 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](https://www.anaconda.com/)) ### 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](https://pytorch.org/): ``` 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](https://github.com/daniilidis-group/neural_renderer) package only has GPU implementation. You are still able to run the demo without GPU. ### 3. Install [neural_renderer](https://github.com/daniilidis-group/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](https://github.com/timesler/facenet-pytorch): This package is optional for the demo. It allows automatic human face detection. ``` pip install facenet-pytorch ``` ## Datasets 1. [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) face dataset. Please download the original images (`img_celeba.7z`) from their [website](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) and run `celeba_crop.py` in `data/` to crop the images. 2. Synthetic face dataset generated using [Basel Face Model](https://faces.dmi.unibas.ch/bfm/). This can be downloaded using the script `download_synface.sh` provided in `data/`. 3. Cat face dataset composed of [Cat Head Dataset](http://academictorrents.com/details/c501571c29d16d7f41d159d699d0e7fb37092cbd) and [Oxford-IIIT Pet Dataset](http://www.robots.ox.ac.uk/~vgg/data/pets/) ([license](https://creativecommons.org/licenses/by-sa/4.0/)). This can be downloaded using the script `download_cat.sh` provided in `data/`. 4. Synthetic car dataset generated from [ShapeNet](https://shapenet.org/) cars. The images are rendered from with random viewpoints from the top, where the cars are primarily oriented vertically. This can be downloaded using the script `download_syncar.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](https://arxiv.org/abs/1604.02878) provided in [facenet-pyt
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