How to Estimate 3D Human Poses with PyTorch

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

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)

Note: This repository has been updated and is different from the method discribed in the paper. To fully reproduce the results in the paper, please checkout the original torch implementation or our pytorch re-implementation branch (slightly worse than torch). We also provide a clean 2D hourglass network branch.

The updates include:

  • Change network backbone to ResNet50 with deconvolution layers (Xiao et al. ECCV2018). Training is now about 3x faster than the original hourglass net backbone (but no significant performance improvement).
  • Change the depth regression sub-network to a one-layer depth map (described in our StarMap project).
  • Change the Human3.6M dataset to official release in ECCV18 challenge.
  • Update from python 2.7 and pytorch 0.1.12 to python 3.6 and pytorch 0.4.1.



The code was tested with Anaconda Python 3.6 and PyTorch v0.4.1. After install Anaconda and Pytorch:

Clone the repo:

git clone POSE_ROOT

Install dependencies (opencv, and progressbar):

conda install --channel opencv
conda install --channel progress

Disable cudnn for batch_norm (see issue):

# PYTORCH=/path/to/pytorch
# for pytorch v0.4.0
sed -i "1194s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/
# for pytorch v0.4.1
sed -i "1254s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/

Optionally, install tensorboard for visializing training.

pip install tensorflow


  • Download our pre-trained model and move it to models.
  • Run python --demo /path/to/image/or/image/folder [--gpus -1] [--load_model /path/to/model].

--gpus -1 is for CPU mode. 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.

Benchmark Testing

To test our model on Human3.6 dataset run

python --exp_id test --task human3d --dataset fusion_3d --load_model ../models/fusion_3d_var.pth --test --full_test

The expected results should be 64.55mm.


Prepare the training data:

Stage1: Train 2D pose only. model, log

python --exp_id mpii
  • Stage2: Train on 2D and 3D data without geometry loss (drop LR at 45 epochs). model, log
python --exp_id fusion_3d --task human3d --dataset fusion_3d --ratio_3d 1 --weight_3d 0.1 --load_model ../exp/mpii/model_last.pth --num_epoch 60 --lr_step 45
  • Stage3: Train with geometry loss. model, log
python --exp_id fusion_3d_var --task human3d --dataset fusion_3d --ratio_3d 1 --weight_3d 0.1 --weight_var 0.01 --load_model ../models/fusion_3d.pth  --num_epoch 10 --lr 1e-4


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}

Download Details:

Author: xingyizhou
Source Code: 
License: GPL-3.0 license

#machinelearning #python #pytorch #3d 

How to Estimate 3D Human Poses with PyTorch
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