Angjoo Kanazawa, Michael J. Black, David W. Jacobs, Jitendra Malik CVPR 2018
- Python 2.7
- TensorFlow tested on version 1.3
virtualenv venv_hmr
source venv_hmr/bin/activate
pip install -U pip
deactivate
source venv_hmr/bin/activate
pip install -r requirements.txt
With GPU:
pip install tensorflow-gpu==1.3.0
Without GPU:
pip install tensorflow==1.3.0
- Download the pre-trained models
wget https://people.eecs.berkeley.edu/~kanazawa/cachedir/hmr/models.tar.gz && tar -xf models.tar.gz
- Run the demo
python -m demo --img_path data/coco1.png
python -m demo --img_path data/im1954.jpg
On images that are not tightly cropped, you can run
openpose and supply
its output json (run it with --write_json option).
When json_path is specified, the demo will compute the right scale and bbox center to run HMR:
python -m demo --img_path data/random.jpg --json_path data/random_keypoints.json
(The demo only runs on the most confident bounding box, see src/util/openpose.py:get_bbox)
Coming soon.
If you use this code for your research, please consider citing:
@inProceedings{kanazawaHMR18,
title={End-to-end Recovery of Human Shape and Pose},
author = {Angjoo Kanazawa
and Michael J. Black
and David W. Jacobs
and Jitendra Malik},
booktitle={Computer Vision and Pattern Regognition (CVPR)},
year={2018}
}