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https://github.com/una-dinosauria/3d-pose-baseline
A simple baseline for 3d human pose estimation in tensorflow. Presented at ICCV 17.
https://github.com/una-dinosauria/3d-pose-baseline
3d-vision baseline computer-vision iccv-17 iccv-2017 tensorflow
Last synced: about 19 hours ago
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A simple baseline for 3d human pose estimation in tensorflow. Presented at ICCV 17.
- Host: GitHub
- URL: https://github.com/una-dinosauria/3d-pose-baseline
- Owner: una-dinosauria
- License: mit
- Created: 2017-04-20T23:05:16.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2020-09-26T02:27:50.000Z (over 4 years ago)
- Last Synced: 2025-02-02T21:55:00.245Z (about 21 hours ago)
- Topics: 3d-vision, baseline, computer-vision, iccv-17, iccv-2017, tensorflow
- Language: Python
- Homepage:
- Size: 319 KB
- Stars: 1,430
- Watchers: 38
- Forks: 355
- Open Issues: 32
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## 3d-pose-baseline
This is the code for the paper
Julieta Martinez, Rayat Hossain, Javier Romero, James J. Little.
_A simple yet effective baseline for 3d human pose estimation._
In ICCV, 2017. https://arxiv.org/pdf/1705.03098.pdf.
The code in this repository was mostly written by
[Julieta Martinez](https://github.com/una-dinosauria),
[Rayat Hossain](https://github.com/rayat137) and
[Javier Romero](https://github.com/libicocco).
We provide a strong baseline for 3d human pose estimation that also sheds light
on the challenges of current approaches. Our model is lightweight and we strive
to make our code transparent, compact, and easy-to-understand.
### Dependencies
* Python ≥ 3.5
* [cdflib](https://github.com/MAVENSDC/cdflib)
* [tensorflow](https://www.tensorflow.org/) 1.0 or later
### First of all
1. Watch our video: https://youtu.be/Hmi3Pd9x1BE
2. Clone this repository
```bash
git clone https://github.com/una-dinosauria/3d-pose-baseline.git
cd 3d-pose-baseline
mkdir -p data/h36m/
```
3. Get the data
Go to http://vision.imar.ro/human3.6m/, log in, and download the `D3 Positions` files for subjects `[1, 5, 6, 7, 8, 9, 11]`,
and put them under the folder `data/h36m`. Your directory structure should look like this
```bash
src/
README.md
LICENCE
...
data/
└── h36m/
├── Poses_D3_Positions_S1.tgz
├── Poses_D3_Positions_S11.tgz
├── Poses_D3_Positions_S5.tgz
├── Poses_D3_Positions_S6.tgz
├── Poses_D3_Positions_S7.tgz
├── Poses_D3_Positions_S8.tgz
└── Poses_D3_Positions_S9.tgz
```
Now, move to the data folder, and uncompress all the data
```bash
cd data/h36m/
for file in *.tgz; do tar -xvzf $file; done
```
Finally, download the `code-v1.2.zip` file, unzip it, and copy the `metadata.xml` file under `data/h36m/`
Now, your data directory should look like this:
```bash
data/
└── h36m/
├── metadata.xml
├── S1/
├── S11/
├── S5/
├── S6/
├── S7/
├── S8/
└── S9/
```
There is one little fix we need to run for the data to have consistent names:
```bash
mv h36m/S1/MyPoseFeatures/D3_Positions/TakingPhoto.cdf \
h36m/S1/MyPoseFeatures/D3_Positions/Photo.cdf
mv h36m/S1/MyPoseFeatures/D3_Positions/TakingPhoto\ 1.cdf \
h36m/S1/MyPoseFeatures/D3_Positions/Photo\ 1.cdf
mv h36m/S1/MyPoseFeatures/D3_Positions/WalkingDog.cdf \
h36m/S1/MyPoseFeatures/D3_Positions/WalkDog.cdf
mv h36m/S1/MyPoseFeatures/D3_Positions/WalkingDog\ 1.cdf \
h36m/S1/MyPoseFeatures/D3_Positions/WalkDog\ 1.cdf
```
And you are done!
Please note that we are currently not supporting SH detections anymore, only training from GT 2d detections is possible now.
### Quick demo
For a quick demo, you can train for one epoch and visualize the results. To train, run
`python src/predict_3dpose.py --camera_frame --residual --batch_norm --dropout 0.5 --max_norm --evaluateActionWise --epochs 1`
This should take about <5 minutes to complete on a GTX 1080, and give you around 56 mm of error on the test set.
Now, to visualize the results, simply run
`python src/predict_3dpose.py --camera_frame --residual --batch_norm --dropout 0.5 --max_norm --evaluateActionWise --epochs 1 --sample --load 24371`
This will produce a visualization similar to this:
### Training
To train a model with clean 2d detections, run:
`python src/predict_3dpose.py --camera_frame --residual --batch_norm --dropout 0.5 --max_norm --evaluateActionWise`
This corresponds to Table 2, bottom row. `Ours (GT detections) (MA)`
### Citing
If you use our code, please cite our work
```
@inproceedings{martinez_2017_3dbaseline,
title={A simple yet effective baseline for 3d human pose estimation},
author={Martinez, Julieta and Hossain, Rayat and Romero, Javier and Little, James J.},
booktitle={ICCV},
year={2017}
}
```
### Other implementations
* [Pytorch](https://github.com/weigq/3d_pose_baseline_pytorch) by [@weigq](https://github.com/weigq)
* [MXNet/Gluon](https://github.com/lck1201/simple-effective-3Dpose-baseline) by [@lck1201](https://github.com/lck1201)
### Extensions
* [@ArashHosseini](https://github.com/ArashHosseini) maintains [a fork](https://github.com/ArashHosseini/3d-pose-baseline) for estimating 3d human poses using the 2d poses estimated by either [OpenPose](https://github.com/ArashHosseini/openpose) or [tf-pose-estimation](https://github.com/ildoonet/tf-pose-estimation) as input.
### License
MIT