Skip to content

Talegqz/unsupervised_co_part_segmentation

Repository files navigation

Unsupervised Co-part Segmentation through Assembly [ICML2021]

Python Pytorch

This repository contains the source code for the paper Unsupervised Co-part Segmentation through Assembly by Qingzhe Gao, Bin Wang, Libin Liu, Baoquan Chen.

avatar

Prerequisites

Our code has been tested on Ubuntu 18.04. Before starting, please configure your Anaconda environment by

conda  create -n unsup-seg python=3.6
conda activate unsup-seg 
pip install -r requirements.txt

Training

Datasets

Taichi-HD and VoxCeleb. Follow the instructions from https://github.com/AliaksandrSiarohin/video-preprocessing.

Run

There are two configuration files in config folder. For Taichi dataset, change config/taichi.json to set save path and data path for training, then run:

CUDA_VISIBLE_DEVICES=0 python train.py --arg_file config_file/taichi.json -b 2

Training on your own dataset

  1. Resize all the videos to the same size, the videos can be in '.gif', '.mp4' or folder with images. We recommend '.png' format for better I/O performance and loss-less.
  2. Create a folder data/dataset_name with 2 subfolders train and test, put training videos in the train and testing in the test.
  3. Create a config config/dataset_name.json based on config/taichi.json .

Evaluation

Download pretrained models and evaluation data from OneDrive. Put model on save_modeland evaluation data in evaluation_data

Quantitative evaluation

To get quantitative evaluation, run:

python evaluation.py --dataset_name vox --model_path save_model/vox --data_path evaluation_data/taichi_and_vox

Quantitative evaluation

To do segmentation on video, run:

python test.py --test_dir your_taichi_path/test/xxxx.mp4 --checkpoint_path save_model/taichi --out_dir result/out --part_numb 11 

Driving static image

To Driving static image (the result need to be improved), run:

python driving_image.py --driving_image driving_image_path --source_image source_image_path --checkpoint_path save_model/taichi --out_dir result/driving_out --part_numb 11 

Acknowledgements

Some code is adapted from first-order-model and motion-cosegmentation by @ AliaksandrSiarohin

Citation

If you use this code for your research, please cite our paper:

@InProceedings{pmlr-v139-gao21c,
  title = 	 {Unsupervised Co-part Segmentation through Assembly},
  author =       {Gao, Qingzhe and Wang, Bin and Liu, Libin and Chen, Baoquan},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {3576--3586},
  year = 	 {2021},
  editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
}

About

[ICML2021] Unsupervised Co-part Segmentation through Assembly

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Languages