Code for Learning Correspondence from the Cycle-consistency of Time (CVPR 2019, Oral). The code is developed based on the PyTorch framework.

If you use our code in your research or wish to refer to the baseline results, please use the following BibTeX entry.

@inproceedings{CVPR2019_CycleTime,
    Author = {Xiaolong Wang and Allan Jabri and Alexei A. Efros},
    Title = {Learning Correspondence from the Cycle-Consistency of Time},
    Booktitle = {CVPR},
    Year = {2019},
}

Please read DATASET.md for downloading and preparing the VLOG dataset for training and DAVIS dataset for testing.

Replace the input list in train_video_cycle_simple.py in the home folder as:

    params['filelist'] = 'YOUR_DATASET_FOLDER/vlog_frames_12fps.txt'

Then run the following code:

    python train_video_cycle_simple.py --checkpoint pytorch_checkpoints/release_model_simple

Replace the input list in test_davis.py in the home folder as:

    params['filelist'] = 'YOUR_DATASET_FOLDER/davis/DAVIS/vallist.txt'

Set up the dataset path YOUR_DATASET_FOLDER in run_test.sh . Then run the testing and evaluation code together:

    sh run_test.sh

Our trained model can be downloaded from here. The testing results for this model is:

The geotnf code was modified from WeakAlign.