  We tackle the problem of modeling sequential visual phenomena. Given examples
of a phenomena that can be divided into discrete time steps, we aim to take an
input from any such time and realize this input at all other time steps in the
sequence. Furthermore, we aim to do this without ground-truth aligned sequences
-- avoiding the difficulties needed for gathering aligned data. This
generalizes the unpaired image-to-image problem from generating pairs to
generating sequences. We extend cycle consistency to loop consistency and
alleviate difficulties associated with learning in the resulting long chains of
computation. We show competitive results compared to existing image-to-image
techniques when modeling several different data sets including the Earth's
seasons and aging of human faces.
