  We propose to learn a probabilistic motion model from a sequence of images.
Besides spatio-temporal registration, our method offers to predict motion from
a limited number of frames, useful for temporal super-resolution. The model is
based on a probabilistic latent space and a novel temporal dropout training
scheme. This enables simulation and interpolation of realistic motion patterns
given only one or any subset of frames of a sequence. The encoded motion also
allows to be transported from one subject to another without the need of
inter-subject registration. An unsupervised generative deformation model is
applied within a temporal convolutional network which leads to a diffeomorphic
motion model, encoded as a low-dimensional motion matrix. Applied to cardiac
cine-MRI sequences, we show improved registration accuracy and
spatio-temporally smoother deformations compared to three state-of-the-art
registration algorithms. Besides, we demonstrate the model's applicability to
motion transport by simulating a pathology in a healthy case. Furthermore, we
show an improved motion reconstruction from incomplete sequences compared to
linear and cubic interpolation.
