Generalizing Monocular 3D Human Pose Estimation in the Wild
This repository is the implementation of the work presented in:
Luyang Wang, Yan Chen, Zhenhua Guo, Keyuan Qian, Mude Lin, Hongsheng Li, Jimmy S. Ren, Generalizing Monocular 3D Human Pose Estimation in the Wild(arXiv:1904.05512)
Watch Our Video on YouTube.
Dependencies
Tensorflow >= 1.4.1
Pytorch >= 0.3.1
Numpy = 1.14.3
CV2 = 3.4.1
Dataset
You can download our processed datasets in the list. We train the 3D Label Generator with Human3.6M dataset and Unity dataset. In addition, We train the Baseline Network with MPII/LSP/AIChallenger/Human3.6M datasets. Note that we provided the MPII/LSP/AIChallenger/Human3.6M datasets with high-quality 3D labels, available through Google Drive.
Guidelines
Download the datasets. All the compressed files suffixes are tar.gz.
tar -zxvf xxx.tar.gz
See more details here.
Pre-trained Model
We also provide a model pre-trained on 3D Label Generator and Baseline Network, available through Baidu Cloud.
Installation
Clone this repository and download our processed datasets.
git clone https://github.com/llcshappy/Monocular-3D-Human-Pose.git
Useage
3D Label Generator
The code of 3D Label Generator was tested with Anaconda Python3.6 and Tensorflow. After install Anaconda and Tensorflow:
Step 1. Open the 3DLabelGen folder:
cd 3DLabelGen/
Step2. Training Stereoscopic View Synthesis Subnetwork
You need to generate the right-view 2D pose.
python2 gen_right.py
Train the subnetwork
./left2right.sh
Step3. Training 3D Pose Reconstruction Subnetwork
Train the subnetwork
./3DPose.sh
Step4. Geometric Search Scheme
See more details of the geometric search scheme in our paper. Please input the action in script 'search_h36m.py'
# Input the action here
action = 'WalkTogether'
Then run this script.
python2 search_h36m.py
Quick Demo
You can run the following code to see the quick demo of the 3D Label Generator.
./demo.sh
Quick Demo
You can run the following code to see the quick demo of our trained Baseline Network.
./demo.sh
Visualization
Citation
@article{wang2019generalizing,
title={Generalizing Monocular 3D Human Pose Estimation in the Wild},
author={Wang, Luyang and Chen, Yan and Guo, Zhenhua and Qian, Keyuan and Lin, Mude and Li, Hongsheng and Ren, Jimmy S},
journal={arXiv preprint arXiv:1904.05512},
year={2019}
}