This is a PyTorch implementation of the Computers & Graphics paper A Study of Deep Single Sketch-Based Modeling: View/Style Invariance, Sparsity and Latent Space Disentanglement.
To get started, simply clone the repo and run the setup bash script, which will take care of installing all packages and dependencies.
./setup.sh
Sometimes, you may need to install the following packages manually.
curl -LO https://github.com/NVIDIA/cub/archive/1.10.0.tar.gz
tar xzf 1.10.0.tar.gz
export CUB_HOME=$PWD/cub-1.10.0
pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable"
In our project, we store data according to the following structure:
data/
<chairs>/
samples/
<instance_name>.npz
meshes/
<instance_name>.obj
renders/
<instance_name>/
naive_mad/
base/
azi_0_elev_10_0001.jpg
...
bias/
azi_-5_elev_15_0001.jpg
...
sty_mad/
base/
azi_0_elev_10_0001.jpg
...
bias/
azi_-5_elev_15_0001.jpg
...
sil_mad/
base/
azi_0_elev_10__sil.png0001.png
...
bias/
azi_-5_elev_15__sil.png0001.png
...
We provide pre-processed and subsampled ShapeNet data for chairs to get you started (124GB).
Simply download it and unzip it in the data/ folder and make sure the folder is arranged according to the above structure to get going.
You can train a single-view reconstruction model for chairs with regression loss by running
python train_svr_reg.py -e experiments/chairs_svr_reg/
Note that, running the script above will overwrite the pretrained checkpoint.
Once the model is trained, you can generate the 3D shape by running
python reconstruct_svr_reg.py -e experiments/chairs_svr_reg/
You can train a single-view reconstruction model for chairs with mask by running
python train_svr_mask.py -e experiments/chairs_svr_mask/
Note that, running the script above will overwrite the pretrained checkpoint.
Once the model is trained, you can generate the 3D shape by running
python reconstruct_svr_reg.py -e experiments/chairs_svr_reg/
If you have any questions, please contact Yue Zhong: zysyly@163.com