ankitshah009 / pose-hg-3d

Code repository for Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach

Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach

This repository includes code 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)

The code is developed upon Stacked Hourglass Network.

[New] Checkout our PyTorch implementation.

Contact: [email protected]

Requirements

• cudnn
• Torch7 with hdf5 and image
• Python with h5py and opencv

Testing

• Download our pre-trained model and move it to models.
• Run th demo.lua /path/to/image.

We provide example images in src/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.

Training

• Prepare the training data:

  • Download our pre-processed Human3.6M dataset here. The main part of the data pre-processing code is in src/Tools/h36mPreprocessing.m. We Converted Human3.6M dataset to .jpg files with 5x down-sampling.
  • Run python GetH36M.py in src/Tools/ to convert H36M annotations to hdf5 format.
  • Run python GetMPI-INF-3D.py in src/Tools/ to convert 3DHP annotations to hdf5 format. (or set valid3DHP in opt.lua false if you don't evaluate on this dataset)

• Stage1: Train the 2D hourglass component

cd src
th main.lua -expID Stage1 -dataset fusion -task pose-hgreg-3d -netType hgreg-3d -varWeight 0.0 -regWeight 0.0  -nEpochs 60

Our results of this stage is provided here. Most of the experiments in the paper are based on this model.

• Stage2: Train without Geometry loss (drop LR at 40 epochs)

th main.lua -expID Stage2 -dataset fusion -task pose-hgreg-3d -loadModel ../models/HGRegS2M2M2_60.t7 -varWeight 0.0 -regWeight 0.1 -dropLR 40 -nEpochs 50

• Stage3: Train with Geometry loss

th main.lua -expID Stage3 -dataset fusion -task pose-hgreg-3d -loadModel ../exp/fusion/Stage2/model_50.t7 -varWeight 0.01 -regWeight 0.1 -LR 2.5e-5 -nEpochs 10`

Citation

@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}
}