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
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 can be found here.
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.
[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
[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
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}
}