EgoGAN: Generative Adversarial Network for Future Hand Segmentation from Egocentric Video (ECCV 2022)
This is the official code release for our ECCV2022 paper on introducing a novel task of predicting a time series of future hand masks from egocentric videos, together with the first deep generative model (EgoGAN) that generate egocentric motion cues for visual anticipations.
[Paper] [Supplement] [Project Page] [Poster] [Presentation]
Our method requires the same dependencies as SlowFast. We refer to the official implementation fo SlowFast for installation details.
conda env create -f environment.yml
conda activate egogan
python tools/run_net.py --cfg /path/to/Ego4D-Future-Hand-Prediction/configs/Ego4D/I3D_8x8_R50.yaml OUTPUT_DIR /path/to/ego4d-hand_ant/output/- Evaluation function
| Directory | Location | Description |
|---|---|---|
| cropped_videos_ant | ./slowfast/datasets/ego4dhand.py | Put your rescaled video clips in this folder |
| PATH_TO_DATA_DIR: ../data-path/ | ./configs/Ego4D/I3D_8x8_R50.yaml | Put your cropped_videos_ant folder and annotation folders under this path |
| OUTPUT_DIR: ../checkpoints/ | ./configs/Ego4D/I3D_8x8_R50.yaml ./tools/test_net.py | Define store location of checkpoints and output file |
| SAVE_RESULTS_PATH: output.pkl | ./configs/Ego4D/I3D_8x8_R50.yaml ./tools/test_net.py | Define output file name |
If you use this code for your research, please cite our paper:
Generative Adversarial Network for Future Hand Segmentation from Egocentric Video.
Wenqi Jia,
Miao Liu,
James Rehg.
In ECCV 2022.
Bibtex:
@inproceedings{jia2022generative,
title={Generative Adversarial Network for Future Hand Segmentation from Egocentric Video},
author={Jia, Wenqi and Liu, Miao and Rehg, James M.},
booktitle={ECCV},
year={2022}
}
Please refer to the future hand prediction repo for more details! Check our leaderboard here.