The official implementation of paper: Estimating Egocentric 3D Human Pose in Global Space.
If you find this repository useful, please cite:
@InProceedings{Wang_2021_ICCV,
author = {Wang, Jian and Liu, Lingjie and Xu, Weipeng and Sarkar, Kripasindhu and Theobalt, Christian},
title = {Estimating Egocentric 3D Human Pose in Global Space},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {11500-11509}
}
- Install pytorch 1.4+ with cuda support.
- Run
mkdir networks/logsand download the trained VAE model into directorynetworks/logsfrom here. - Run
mkdir dataand download the processed test sequences into directorydatafrom here. - Run the test on the sequences:
python optimize_whole_sequence.py --data_path data/jian3
python optimize_whole_sequence.py --data_path data/studio-jian1
python optimize_whole_sequence.py --data_path data/studio-jian2
python optimize_whole_sequence.py --data_path data/studio-lingjie1
python optimize_whole_sequence.py --data_path data/studio-lingjie2
If you want to train the motion vae:
- run
mkdir AMASSDataConverter && cd AMASSDataConverter && mkdir pkl_data - download the processed dataset to directory
pkl_datafrom here. - see directory
networks
If you want to run on your own dataset,
you need to firstly preprocess the data with repo: MakeDataForOptimization.
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