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UNIST

Pytorch Implementation of UNIST: Unpaired Neural Implicit Shape Translation Network, Qimin Chen, Johannes Merz, Aditya Sanghi, Hooman Shayani, Ali Mahdavi-Amiri and Hao (Richard) Zhang, CVPR 2022

Paper | Video | Project page

Dependencies

Install required dependencies, run

conda env create -f environment.yml

Our code has been tested with Python 3.6, Pytorch 1.6.0, CUDA 10.1 and cuDNN 7.0 on Ubuntu 18.04.

Datasets

Datasets and model weights can be found here. Description of each key in hdf5 file can be found below. For custom datasets, please follow the below steps for preparation.

2D dataset

[key] - [description]
file_names - file name of each image
pixels - image in gray scale, (num_image, 256, 256)
points_128 - query points, (num_image, 16384, 2)
values_128 - inside / outside values of query points, (num_image, 16384, 1)
# Note that we don't employ progressive training in 2D experiments so you can ignore points_64 and values_64

We provide code of sampling query points near/inside/outside boundary with a ratio of 3:2:1 from a 256x256 image in sampling.py, you can adjust accordingly.

3D dataset

[key] - [description]
file_names - file name of each shape, (num_shape, 1)
pixels - image in gray scale, (num_shape, 24, 137, 137), not used
voxels - input voxel of each shape, (num_shape, 64, 64, 64, 1)
points_16 - query points sampled from 16^3 voxel, (num_shape, 4096, 3)
values_16 - inside / outside values of query points, (num_shape, 4096, 1)
points_32 - query points sampled from 32^3 voxel, (num_shape, 4096, 3)
values_32 - inside / outside values of query points, (num_shape, 4096, 1)
points_64 - query points sampled from 64^3 voxel, (num_shape, 16384, 3)
values_64 - inside / outside values of query points, (num_shape, 16384, 1)

Please refer to IM-NET for detailed sampleing code. Sampling code is verified and can be readily use by simply changing the path.

Usage

We provide instructions for training and testing 2D translation on A-H dataset and 3D translations on chair-table dataset, below instruction works for other domain pairs.

2D Experiments

[Note] You need to train autoencoding first, then translation.

[First] To train autoencoder, use the following command to train on images of resolution 128^2.

python run_2dae.py --train --epoch 800 --dataset A-H --sample_im_size 128

To test reconstruction, use the following command

python run_2dae.py  --dataset A-H --sample_dir outputs --sample_im_size 128

[Second] To train translation, first use the following command to extract feature grid

python run_2dae.py --train --getz --dataset A-H --sample_im_size 128  # training data
python run_2dae.py --getz --dataset A-H --sample_im_size 128          # testing data

then use the following command to train translation

python run_2dgridtranslator.py --train --epoch 1200 --dataset A-H --batch_size 128

To test translation, use the following command

python run_2dgridtranslator.py --dataset A-H --sample_dir outputs

3D Experiments

[First] To train autoencoder, use the following commands for progressive training.

python run_ae.py --train --epoch 300 --dataset chair-table --sample_vox_size 16
python run_ae.py --train --epoch 300 --dataset chair-table --sample_vox_size 32
python run_ae.py --train --epoch 600 --dataset chair-table --sample_vox_size 64

To test reconstruction (default on voxel of resolution 256^3), use the following command

python run_ae.py --dataset chair-table --sample_dir outputs

[Second] To train translation, first use the following command to extract feature grid

python run_ae.py --train --getz --dataset chair-table  # training data
python run_ae.py --getz --dataset chair-table          # testing data

then use the following command to train translation

python run_3dgridtranslator.py --train --epoch 4800 --dataset chair-table --batch_size 128

To test translation, use the following command

python run_3dgridtranslator.py --dataset chair-table --sample_dir outputs

Citation

Please cite our paper if you find this code or research relevant:

  @inproceedings{chen2022unist,
    title={UNIST: Unpaired Neural Implicit Shape Translation Network},
    author={Chen, Qimin and Merz, Johannes and Sanghi, Aditya and Shayani, Hooman and Mahdavi-Amiri, Ali and Zhang, Hao},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    year={2022}
  }