Keras implementation of 3d-pose-baseline.
from WiderFaceDataset
python download_model.py
Predict using Caffe OpenPose and Keras
python predict.py
Dump training data from 3d-pose-baseline using export_dataset.py
https://github.com/una-dinosauria/3d-pose-baseline
python plot.py
Training using Keras
python train.py
This is a pretrained output
http://www.abars.biz/keras/3d-pose-baseline.hdf5
http://www.abars.biz/keras/3d-pose-baseline-mean.h5
python convert.py
http://www.abars.biz/keras/3d-pose-baseline.caffemodel
http://www.abars.biz/keras/3d-pose-baseline.prototxt
3d-pose-baseline predict 3d pose from 2d pose.
Input is 16 keypoint. Each keypoint has 2 axis.
Output is 16 keypoint. Eash keypoint has 3 axis.
Output should be denormalize using mean value.
Mean value has 32 keypoint, So you should remove unused dimension. Mean value is sparse.
https://github.com/una-dinosauria/3d-pose-baseline
@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} }
OpenPose to 3dpose
https://github.com/miu200521358/3d-pose-baseline-vmd/blob/master/src/openpose_3dpose_sandbox_vmd.py