This work is superseded by DeepPrior++

DeepPrior - Accurate and Fast 3D Hand Pose Estimation

Author: Markus Oberweger oberweger@icg.tugraz.at

Requirements:

OS
- Ubuntu 14.04
- CUDA 7
via Ubuntu package manager:
- python2.7
- python-matplotlib
- python-scipy
- python-pil
- python-numpy
- python-vtk6
- python-pip
- python-vtk6
via pip install:
- scikit-learn
- progressbar
- psutil
- theano (0.8)
Camera driver
- OpenNI for Kinect
- DepthSense SDK for Creative Senz3D.

For a description of our method see:

M. Oberweger, P. Wohlhart, and V. Lepetit. Hands Deep in Deep Learning for Hand Pose Estimation. In Computer Vision Winter Workshop, 2015.

Setup:

Put dataset files into ./data (e.g. ICVL dataset, or NYU dataset )
Goto ./src and see the main file test_realtimepipeline.py how to handle the API
Camera interface for the Creative Senz3D is included in ./src/util. Build them with cmake . && make.

Pretrained models:

Download pretrained models for ICVL and NYU dataset.

Datasets:

The ICVL dataset is trained for a time-of-flight camera, and the NYU dataset for a structured light camera. The annotations are different. See the papers for it.

D. Tang, H. J. Chang, A. Tejani, and T.-K. Kim. Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture. In Conference on Computer Vision and Pattern Recognition, 2014.

J. Tompson, M. Stein, Y. LeCun, and K. Perlin. Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks. ACM Transactions on Graphics, 33, 2014.