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Code for ICCV 2019 Paper: "Predicting 3D Human Dynamics from Video"

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최종 버전 다운로드 (.zip)

- 0502.mp4
- - __init__.py
  - run_video.py
  - tester_pred.py
- - install_alphapose.sh
  - install_nmr.sh
- - __init__.py
  - batch_lbs.py
  - batch_smpl.py
  - projection.py
- - __init__.py
  - common.py
  - render_utils.py
  - smooth_bbox.py
  - torch_utils.py
  - video.py
- __init__.py
- config.py
- extract_tracks.py
- models.py
- omega.py
- ops.py
- renderer.py
.gitignore
demo.py
LICENSE
README.md
requirements.txt

// repository documentation

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# Predicting 3D Human Dynamics from Video

Jason Y. Zhang, Panna Felsen, Angjoo Kanazawa, Jitendra Malik

University of California, Berkeley

[Project Page](https://jasonyzhang.com/phd/)

![Teaser Image](https://jasonyzhang.com/phd/assets/img/overview.jpg)

Requirements:
* Python 3 (tested on 3.6.8)
* Tensorflow (tested on 1.15)
* Pytorch for NMR (tested on 1.3.0)
* CUDA (tested on 10.0)
* ffmpeg (tested on 3.4.6)

### License:

Our code is licensed under BSD. Note that the SMPL model and any datasets still
fall under their respective licenses.

### Installation:
```bash
virtualenv venv_phd -p python3
source venv_phd/bin/activate
pip install -U pip
pip install numpy tensorflow-gpu==1.15.0
pip install torch==1.3.0  # Make sure the wheel corresponds to your CUDA Version
pip install -r requirements.txt
cd src/external
sh install_nmr.sh
```

Download the model weights from [this Google Drive link](https://drive.google.com/file/d/1_sipXE-FNs_08YCPFxFlLauHJcqzny7x/view?usp=sharing).
You should place them in `phd/models`.

## Running Demo

### Penn Action

Download the [Penn Action dataset](http://dreamdragon.github.io/PennAction/).
You should place or symlink the dataset to `phd/data/Penn_Action`.

#### Running on one subsequence
`--vid_id 0104` runs the model on video 0104 in Penn Action. The public model is
conditioned on 15 images, so `--start_frame 60` starts the conditioning window 
at 60, and future prediction will start on frame 76. `--ar_length 25` sets the
number of future predictions at 25, which is the prediction length the model
was trained on. You can also try increasing `ar_length`, which usually looks
reasonable until 35.

```
python demo.py --load_path models/phd.ckpt-199269 --vid_id 0104 --ar_length 25 --start_frame 60
```

For reference, [this](https://jasonyzhang.com/phd/assets/vid/penn_action-0104_AR25_60-100_fps5.mp4)
should be your output.

#### Running on multiple subsequences

You can also run at multiple starting points in the same sequence.
`--start_frame 0 --skip_rate 5` will run starting at frame 0, frame 5, frame 10,
etc.

```
python demo.py --load_path models/phd.ckpt-199269 --vid_id 0104 --ar_length 25 --start_frame 0 --skip_rate 5 
```
For reference, [this](https://jasonyzhang.com/phd/assets/vid/0104.zip) should be your output.

### Running on Any Video

To run on a generic video, you will need a tracklet around the person. We extract the tracklet using PoseFlow.

Follow directions to download AlphaPose and Model Weights from https://github.com/MVIG-SJTU/AlphaPose.

Roughly, that should entail:
1. Clone the repo to `src/external`
2. Build AlphaPose using `python setup.py build develop --user`
3. Download pre-trained weights to the specified directories. Use the ResNet50 Fast Pose from the Model Zoo.

Steps 1. and 2. can be done by running `sh install_alphapose.sh` in `src/external`

Now you should be able to run the model on any video, eg:
```
python demo.py --load_path models/phd.ckpt-199269 --vid_path data/0502.mp4 --start_frame 0 --ar_length 25
```

## Training Code

Coming soon

phd

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