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, Rayat Hossain and Javier Romero.
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.
- h5py
- tensorflow 1.0 or later
- Watch our video: https://youtu.be/Hmi3Pd9x1BE
- Clone this repository and get the data. We provide the Human3.6M dataset in 3d points, camera parameters to produce ground truth 2d detections, and Stacked Hourglass detections.
git clone https://github.com/una-dinosauria/3d-pose-baseline.git
cd 3d-pose-baseline
mkdir data
cd data
wget https://www.dropbox.com/s/e35qv3n6zlkouki/h36m.zip
unzip h36m.zip
rm h36m.zip
cd ..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 --use_sh --epochs 1
This should take about <5 minutes to complete on a GTX 1080, and give you around 75 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 --use_sh --epochs 1 --sample --load 24371
This will produce a visualization similar to this:
- Setup OpenPose and use
--write_jsonflag to export Pose Keypoints. - Download Pre-trained model below, simply run
python src/openpose_3dpose_sandbox.py --camera_frame --residual --batch_norm --dropout 0.5 --max_norm --evaluateActionWise --use_sh --epochs 200 --load 4874200 --openpose /path/to/openpose/output/json_directory --gif_fps 30 , optional also --verbose 3 for debug,
smoothing curves by median value
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)
To train on Stacked Hourglass detections, run
python src/predict_3dpose.py --camera_frame --residual --batch_norm --dropout 0.5 --max_norm --evaluateActionWise --use_sh
This corresponds to Table 2, next-to-last row. Ours (SH detections) (MA)
On a GTX 1080 GPU, this takes <8 ms for forward+backward computation, and <6 ms for forward-only computation per batch of 64.
We also provide a model pre-trained on Stacked-Hourglass detections, available through google drive
To test the model, decompress the file at the top level of this project, and call
python src/predict_3dpose.py --camera_frame --residual --batch_norm --dropout 0.5 --max_norm --evaluateActionWise --use_sh --epochs 200 --sample --load 4874200
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}
}
MIT