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3dpose_gan

(★ 143)

The authors' implementation of Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint Locations

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Download Latest Version (.zip)

- - show_h36m_results.py
  - show_mpii.py
- demo.py
- eval.py
- eval_pck.py
- evaluation_util.py
- train.py
- actions.txt
- mpii_image_name.txt
- mpii_poses.npy
- - __init__.py
  - evaluator.py
  - posenet.py
  - updater.py
- __init__.py
- gen_epoch_500.npz
- image.png
.gitattributes
.gitignore
LICENSE
README.md
requirements.txt
setup.py

# Installation Guide

1. Get the code

git clone https://github.com/DwangoMediaVillage/3dpose_gan

Downloads the entire project code from GitHub to your computer.

cd 3dpose_gan

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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# Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint Locations

This is the authors' implementation of [Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint Locations
](https://arxiv.org/abs/1803.08244)

![](https://nico-opendata.jp/assets/img/casestudy/3dpose_gan/system.png)

![](https://nico-opendata.jp/assets/img/casestudy/3dpose_gan/mpii.jpg)

## Run Inference for demo (with openpose)

1. Download openpose pretrained model
    * openpose_pose_coco.prototxt
        * https://github.com/opencv/opencv_extra/blob/3.4.1/testdata/dnn/openpose_pose_coco.prototxt
    * pose_iter_440000.caffemodel
        * http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel
2. Run Inference
    * ` python bin/demo.py sample/image.png --lift_model sample/gen_epoch_500.npz --model2d pose_iter_440000.caffemodel --proto2d openpose_pose_coco.prototxt`
    * **Need OpenCV >= 3.4**
        * < 3.3 results extreamly wrong estimation

## Dependencies(Recommended versions)
  - Python 3.6.5
  - Cupy 4.0.0
  - Chainer 4.0.0
  - OpenCV 3.4 (when showing results)
  - git-lfs
    - to download pre-trained model
    - or you can download pre-trained model directory from [https://github.com/DwangoMediaVillage/3dpose_gan/blob/master/sample/gen_epoch_500.npz?raw=true](https://github.com/DwangoMediaVillage/3dpose_gan/blob/master/sample/gen_epoch_500.npz?raw=true)

## Training
#### Human3.6M dataset
  - [x] Unsupervised learning of 3D points from ground truth 2D points

```
    python bin/train.py --gpu 0 --mode unsupervised --dataset h36m --use_heuristic_loss
    ```
  - [ ] Unsupervised learning of 3D points from detected 2D points by Stacked Hourglass

TBA

- [x] Supervised learning of 3D points from ground truth 2D points

```
    python bin/train.py --gpu 0 --mode supervised --activate_func relu --use_bn
    ```

#### MPII dataset
TBA

#### MPI-INF-3DHP dataset
TBA

## Evaluation
```
python bin/eval.py results/hoge/gen_epoch_*.npz
```