https://zju3dv.github.io/iMoCap/

dataset for ECCV 2020 "Motion Capture from Internet Videos"
https://zju3dv.github.io/iMoCap/

Last synced: 3 months ago
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dataset for ECCV 2020 "Motion Capture from Internet Videos"

• Host: GitHub
• URL: https://zju3dv.github.io/iMoCap/
• Owner: zju3dv
• Created: 2020-07-03T12:19:12.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-09-27T14:51:49.000Z (over 4 years ago)
• Last Synced: 2024-08-03T17:12:30.849Z (6 months ago)
• Language: Python
• Size: 35.8 MB
• Stars: 108
• Watchers: 25
• Forks: 11
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-3D-human - iMoCap\

README

        
# Motion Capture from Internet Videos

> [Motion Capture from Internet Videos](https://arxiv.org/pdf/2008.07931.pdf)  

> Junting Dong*, Qing Shuai*, Yuanqing Zhang, Xian Liu, Xiaowei Zhou, Hujun Bao   

> ECCV 2020 

> [Project Page](https://zju3dv.github.io/iMoCap/)

## Datasets

### Internet video dataset

[Download](https://drive.google.com/file/d/1yD9VuKo5g4QDHAcBDli8a7absUVtZCXC/view?usp=sharing)

### Modified Human3.6M dataset 

You can download our modified Human3.6M dataset [here](https://drive.google.com/file/d/10KQTrp-TK6XvZuo27Xz0BPLF38S074co/view?usp=sharing).

### Create your own synthetic data

First, we split the origin videos into different folders, and store the 3D annotations as follows.

```

├── data_2d_h36m_cpn_ft_h36m_dbb.npz

├── joints3d

│   ├── S9_Directions 1.mat

│   ├── S9_Directions.mat

│   ├── ...

│   ├── ...

│   ├── ...

│   ├── S9_WalkTogether 1.mat

│   └── S9_WalkTogether.mat

└── S9

    ├── Directions

    │   ├── Directions.54138969.mp4

    │   ├── Directions.55011271.mp4

    │   ├── Directions.58860488.mp4

    │   └── Directions.60457274.mp4

    ├── Directions1

    │   ├── Directions1.54138969.mp4

    │   ├── Directions1.55011271.mp4

    │   ├── Directions1.58860488.mp4

    │   └── Directions1.60457274.mp4

    |   ......

    ├── WalkTogether

    │   ├── WalkTogether.54138969.mp4

    │   ├── WalkTogether.55011271.mp4

    │   ├── WalkTogether.58860488.mp4

    │   └── WalkTogether.60457274.mp4

    └── WalkTogether1

        ├── ......

```

We use finetune cpn output as our 2D pose from [videopose3d](https://github.com/facebookresearch/VideoPose3D/blob/master/DATASETS.md)

```bash

wget https://dl.fbaipublicfiles.com/video-pose-3d/data_2d_h36m_cpn_ft_h36m_dbb.npz

```

After all, you can generate the synthetic data. More details can be found in the file `script/dataset/sample_h36m.py`.

```bash

python3 script/dataset/sample_h36m.py --video_path /S9

```

## Quantitative evaluation

Our quantitative evaluation includes two parts: match and reconstruction. We provide the evaluation scripts as example.