Dense 3D Regression for Hand Pose Estimation

This respository contains tensorflow implementation of the paper. It is developped and tested on Debian GNU/Linux 8 64-bit.

Requirements:

• python 2.7
• tensorflow == 1.3
• tfplot (for visualization on tf summary files)
• matplotlib >= 2.0.2
• numpy
• opencv >= 2.4 (optional, for cpu visualization)

Data Preparations:

Download the datasets, create soft links for them to exp/data and run python data/${dataset}.py to create the TFRecord files. Details are here.

Usage:

Both training and testing functions are provided by model/hourglass_um_crop_tiny.py. Here is an example:

python model/hourglass_um_crop_tiny.py --dataset 'icvl' --batch_size 40 --num_stack 2 --fea_num 128 --debug_level 2 --is_train True

where the hyper parameter configuration is explained in the source python files.

Results:

We provide the estimation results by the proposed method for ICVL, NYU, MSRA15. They are xyz coordinates in mm, the 2D projection method is in the function xyz2uvd from here. Check here for comparison to other methods. Thanks @xinghaochen for providing the comparison.

Pretrained Models:

Run the script to download and install the corresponding trained model of datasets. $ROOT denote the root path of this project.

cd $ROOT
./exp/scripts/fetch_icvl_models.sh
./exp/scripts/fetch_msra_models.sh
./exp/scripts/fetch_nyu_models.sh

To perform testing, simply run

python model/hourglass_um_crop_tiny.py --dataset 'icvl' --batch_size 3 --num_stack 2 --num_fea 128 --debug_level 2 --is_train False
python model/hourglass_um_crop_tiny.py --dataset 'nyu' --batch_size 3 --num_stack 2 --num_fea 128 --debug_level 2 --is_train False
python model/hourglass_um_crop_tiny.py --dataset 'msra' --pid 0 --batch_size 3 --num_stack 2 --num_fea 128 --debug_level 2 --is_train False

in which msra dataset should use pid to indicate which person to test on. In the testing function, the third augument is used to indicate which model with corresponding training step will be restored. We use step of -1 to indicate our pre-trained model.