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Kinematic Human Pose Regression

This repository implements kinematic-model based 3D pose regression in Pytorch. It is based off of Integral Pose Regression.

Environment

Python Version: 3.6
OS: CentOs7 (Other Linux system is also OK)
CUDA: 9.0 (least 8.0)
PyTorch:0.4.0(see issue JimmySuen#4)

Installation

We recommend installing python from Anaconda, installing pytorch following guide on PyTorch according to your specific CUDA & python version. In addition, you need to install dependencies below.

pip install scipy
pip install matplotlib
pip install opencv-python
pip install easydict
pip install pyyaml

Preparation for Training & Testing

  1. Download Human3.6M(ECCV18 Challenge) image from Human3.6M Dataset and our processed annotation from Baidu Disk (code: kfsm) or Google Drive
  2. Download MPII image from MPII Human Pose Dataset
  3. Download COCO2017 image from COCO Dataset
  4. Download cache file from Dropbox
  5. Organize data like this
${PROJECT_ROOT}
 `-- data
     `-- coco
        |-- images
        |-- annotations
        |-- COCO_train2017_cache
     `-- mpii
        |-- images
        |-- annot
        |-- mpii_train_cache
        |-- mpii_valid_cache
     `-- hm36
        |-- images
        |-- annot
        |-- HM36_train_cache
        |-- HM36_validmin_cache
     `-- hm36_eccv_challenge
        `-- Train
            |-- IMG
            |-- POSE
        `-- Val
            |-- IMG
            |-- POSE
        `-- Test
            |-- IMG
        |-- HM36_eccv_challenge_Train_cache
        |-- HM36_eccv_challenge_Test_cache
        |-- HM36_eccv_challenge_Val_cache

Usage

We have placed some example config files in experiments folder, and you can use them straight forward. Don't modify them unless you know exactly what it means.

Train

For Integral Human Pose Regression, cd to pytorch_projects/integral_human_pose
Kinematic Regression

python3 train.py --cfg=experiments/hm36/resnet50v1_ft/kinematic_d-mh_ps-256/lr1e-3.yaml --da
taroot="../../data/"

Direct Joint Regression

python train.py --cfg=experiments/hm36/resnet50v1_ft/d-mh_ps-256_dj_l1_adam_bs32-4gpus_x140-90-120/lr1e-3.yaml --dataroot=../../data/

By default, logging and model will be saved to log and output folder respectively.

Misc

  • The project is built on old version of pytorch(0.4.0), and currently the latest released one has updated to 1.0.1. So there may be some compatibility problems. Please feel free to submit new issues.