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https://zju3dv.github.io/iMoCap/
dataset for ECCV 2020 "Motion Capture from Internet Videos"
https://zju3dv.github.io/iMoCap/
Last synced: about 2 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 (about 4 years ago)
- Default Branch: master
- Last Pushed: 2020-09-27T14:51:49.000Z (almost 4 years ago)
- Last Synced: 2024-02-10T15:43:22.318Z (5 months ago)
- Language: Python
- Size: 35.8 MB
- Stars: 104
- Watchers: 25
- Forks: 11
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Lists
- 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.