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This work is superseded by DeepPrior++

DeepPrior - Accurate and Fast 3D Hand Pose Estimation

Author: Markus Oberweger [email protected]

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.