Official PyTorch inference code of our CVPR 2024 paper, "3D Human Pose Perception from Egocentric Stereo Videos".
For any questions, please contact the first author, Hiroyasu Akada [hakada@mpi-inf.mpg.de] .
[Project Page] [Benchmark Challenge]
@inproceedings{hakada2024unrealego2,
title = {3D Human Pose Perception from Egocentric Stereo Videos},
author = {Akada, Hiroyasu and Wang, Jian and Golyanik, Vladislav and Theobalt, Christian},
booktitle = {Computer Vision and Pattern Recognition (CVPR)},
year = {2024}
}
You can download the UnrealEgo2/UnrealEgo-RW datasets on our benchmark challenge page.
You can donwload depth data from SfM/Metashape described in our paper.
-
Depth from UnrealEgo-RW test split
bash download_unrealego2_test_sfm.sh bash download_unrealego_rw_test_sfm.sh
Note that these depth data are different from the synthetic depth maps available on our benchmark challenge page.
We tested our code with the following dependencies:
- Python 3.9
- Ubuntu 18.04
- PyTorch 2.0.0
- Cuda 11.7
Please install other dependencies:
pip install -r requirements.txt
You can download our trained models. Please save them in ./log/(experiment_name)
.
bash scripts/test/unrealego2_pose-qa-avg-df_data-ue2_seq5_skip3_B32_lr2-4_pred-seq_local-device_pad.sh
--data_dir [path to the `UnrealEgoData2_test_rgb` dir]
--metadata_dir [path to the `UnrealEgoData2_test_sfm` dir]
Please modify the arguments above. The pose predictions will be saved in ./results/UnrealEgoData2_test_pose (raw and zip versions)
.
-
Model without pre-training on UnrealEgo2
bash scripts/test/unrealego2_pose-qa-avg-df_data-ue-rw_seq5_skip3_B32_lr2-4_pred-seq_local-device_pad.sh --data_dir [path to the `UnrealEgoData_rw_test_rgb` dir] --metadata_dir [path to the `UnrealEgoData_rw_test_sfm` dir]
-
Model with pre-training on UnrealEgo2
bash scripts/test/unrealego2_pose-qa-avg-df_data-ue2_seq5_skip3_B32_lr2-4_pred-seq_local-device_pad_finetuning_epoch5-5.sh --data_dir [path to the `UnrealEgoData_rw_test_rgb` dir] --metadata_dir [path to the `UnrealEgoData_rw_test_sfm` dir]
Please modify the arguments above. The pose predictions will be saved in ./results/UnrealEgoData_rw_test_pose (raw and zip versions)
.
For quantitative results of your methods, please follow the instructions in our benchmark challenge page and submit a zip version.