Results.mp4
This repository contains codes for training and testing the method.
@inproceedings{10.1145/3610548.3618147,
author = {Kang, Taeho and Lee, Kyungjin and Zhang, Jinrui and Lee, Youngki},
title = {Ego3DPose: Capturing 3D Cues from Binocular Egocentric Views},
year = {2023},
isbn = {9798400703157},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3610548.3618147},
doi = {10.1145/3610548.3618147},
abstract = {We present Ego3DPose, a highly accurate binocular egocentric 3D pose reconstruction system. The binocular egocentric setup offers practicality and usefulness in various applications, however, it remains largely under-explored. It has been suffering from low pose estimation accuracy due to viewing distortion, severe self-occlusion, and limited field-of-view of the joints in egocentric 2D images. Here, we notice that two important 3D cues, stereo correspondences, and perspective, contained in the egocentric binocular input are neglected. Current methods heavily rely on 2D image features, implicitly learning 3D information, which introduces biases towards commonly observed motions and leads to low overall accuracy. We observe that they not only fail in challenging occlusion cases but also in estimating visible joint positions. To address these challenges, we propose two novel approaches. First, we design a two-path network architecture with a path that estimates pose per limb independently with its binocular heatmaps. Without full-body information provided, it alleviates bias toward trained full-body distribution. Second, we leverage the egocentric view of body limbs, which exhibits strong perspective variance (e.g., a significantly large-size hand when it is close to the camera). We propose a new perspective-aware representation using trigonometry, enabling the network to estimate the 3D orientation of limbs. Finally, we develop an end-to-end pose reconstruction network that synergizes both techniques. Our comprehensive evaluations demonstrate that Ego3DPose outperforms state-of-the-art models by a pose estimation error (i.e., MPJPE) reduction of 23.1\% in the UnrealEgo dataset. Our qualitative results highlight the superiority of our approach across a range of scenarios and challenges.},
booktitle = {SIGGRAPH Asia 2023 Conference Papers},
articleno = {82},
numpages = {10},
keywords = {3D Human Pose Estimation, Stereo vision, Egocentric, Heatmap},
location = {, Sydney, NSW, Australia, },
series = {SA '23}
}
After downloading UnrealEgo dataset following instructions in UnrealEgo repository, reprocess the dataset for our code.
reprocess_unrealego_data.py parse metadata and process 2D and 3D pose data for Ego3DPose.
python reprocess_unrealego_data.py
Our code is tested in the following environment
- Python 3.8.10
- Ubuntu 20.04
- PyTorch 2.0.1
- Cuda 12.0
You can install required packages with requirements.txt
You can train the models from scratch or use trained weights. The model weights will be saved in ./log/(experiment_name).
bash scripts/train/ego3dpose_heatmap_shared/ego3dpose_heatmap_shared_pos.sh
bash scripts/train/ego3dpose_heatmap_shared/ego3dpose_heatmap_shared_sin.sh
please specify the path to the UnrealEgo dataset in '--data_dir'.
bash scripts/train/ego3dpose_autoencoder/ego3dpose_autoencoder.sh
please specify the path to the UnrealEgo dataset in '--data_dir'. After the training is finished, you will see quantitative results.
If you want to see quantitative results using trained weights, run the command below. This will also output result summary as a text file, which can be used for ploting for comparison of methods.
bash scripts/test/ego3dpose_autoencoder.sh
We also provide our implementation of a previous work, EgoGlass with its body part branch. We uploaded the weights in the trained weights. You can test with pretrained weight with the following script.
bash scripts/test/egoglass/egoglass.sh
You can also train the method with the following script.
bash scripts/train/egoglass/egoglass.sh
UnrealEgo and EgoGlass without its body part branch can be experimented in the UnrealEgo repository
Permission is hereby granted, free of charge, to any person or company obtaining a copy of this software and associated documentation files (the "Software") from the copyright holders to use the Software for any non-commercial purpose. Publication, redistribution and (re)selling of the software, of modifications, extensions, and derivates of it, and of other software containing portions of the licensed Software, are not permitted. The Copyright holder is permitted to publically disclose and advertise the use of the software by any licensee.
Packaging or distributing parts or whole of the provided software (including code, models and data) as is or as part of other software is prohibited. Commercial use of parts or whole of the provided software (including code, models and data) is strictly prohibited. Using the provided software for promotion of a commercial entity or product, or in any other manner which directly or indirectly results in commercial gains is strictly prohibited.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
This code is based on UnrealEgo repository, and thus inherited its license terms. We thank the authors of the UnrealEgo for the permission to share our codes.