MoDeep: A Deep Learning Framework Using Motion Features for Human Pose   Estimation
  In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion, that extends the FLIC dataset with additional motion features. We apply our architecture to this dataset and report significantly better performance than current state-of-the-art pose detection systems. 
Arjun Jain, Jonathan Tompson, Yann LeCun and Christoph Bregler
cs.CV cs.LG cs.NE