  We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and
prediction of human body pose in videos and motion capture. The ERD model is a
recurrent neural network that incorporates nonlinear encoder and decoder
networks before and after recurrent layers. We test instantiations of ERD
architectures in the tasks of motion capture (mocap) generation, body pose
labeling and body pose forecasting in videos. Our model handles mocap training
data across multiple subjects and activity domains, and synthesizes novel
motions while avoid drifting for long periods of time. For human pose labeling,
ERD outperforms a per frame body part detector by resolving left-right body
part confusions. For video pose forecasting, ERD predicts body joint
displacements across a temporal horizon of 400ms and outperforms a first order
motion model based on optical flow. ERDs extend previous Long Short Term Memory
(LSTM) models in the literature to jointly learn representations and their
dynamics. Our experiments show such representation learning is crucial for both
labeling and prediction in space-time. We find this is a distinguishing feature
between the spatio-temporal visual domain in comparison to 1D text, speech or
handwriting, where straightforward hard coded representations have shown
excellent results when directly combined with recurrent units.
