@inproceedings{lehrmann14motion,
  title = {Efficient Non-linear Markov Models for Human Motion},
  booktitle = { Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  abstract = {Dynamic Bayesian networks such as Hidden Markov
  Models (HMMs) are successfully used as probabilistic models
  for human motion. The use of hidden variables makes
  them expressive models, but inference is only approximate
  and requires procedures such as particle filters or Markov
  chain Monte Carlo methods. In this work we propose to instead
  use simple Markov models that only model observed
  quantities. We retain a highly expressive dynamic model by
  using interactions that are nonlinear and non-parametric.
  A presentation of our approach in terms of latent variables
  shows logarithmic growth for the computation of exact loglikelihoods
  in the number of latent states. We validate
  our model on human motion capture data and demonstrate
  state-of-the-art performance on action recognition and motion
  completion tasks.},
  pages = {1314-1321},
  publisher = {IEEE},
  month = jun,
  year = {2014},
  slug = {lehrmann14motion},
  author = {Lehrmann, Andreas M. and Gehler, Peter V. and Nowozin, Sebastian},
  month_numeric = {6}
}