This repository is the PyTorch implementation for the network presented in:

Xingyi Zhou, Qixing Huang, Xiao Sun, Xiangyang Xue, Yichen Wei, Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach ICCV 2017 (arXiv:1704.02447)

Checkout the original torch implementation.

Checkout the clean 2D hourglass network branch.

Contact: [email protected]

• cudnn
• PyTorch
• Python with h5py, opencv and progress
• Optional: tensorboard

• Download our pre-trained model and move it to models.
• Run python demo.py -demo /path/to/image [-loadModel /path/to/image].

We provide example images in images/. For testing your own image, it is important that the person should be at the center of the image and most of the body parts should be within the image.

• Prepare the training data:

  • Download our pre-processed Human3.6M dataset here.
  • Run python GetH36M.py in src/tools/ to convert H36M annotations to hdf5 format.
  • Modify src/ref.py to setup the dataset path.

• Stage1: Train the 2D hourglass component for 60 epochs

python main.py -expID Stage1

Our results of this stage is provided here.

• Stage2: Train without Geometry loss (drop LR at 25 epochs)

python main.py -expID Stage2 -ratio3D 1 -regWeigh 0.1 -loadModel ../exp/Stage1/model_60.pth -nEpochs 30 -dropLR 25

• Stage3: Train with Geometry loss

python main.py -expID Stage3 -ratio3D 1 -regWeigh 0.1 -varWeight 0.01 -loadModel ../exp/Stage2/model_30.pth -LR 2.5e-5 -nEpochs 10

@InProceedings{Zhou_2017_ICCV,
author = {Zhou, Xingyi and Huang, Qixing and Sun, Xiao and Xue, Xiangyang and Wei, Yichen},
title = {Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}