  Video prediction is commonly referred to as forecasting future frames of a
video sequence provided several past frames thereof. It remains a challenging
domain as visual scenes evolve according to complex underlying dynamics, such
as the camera's egocentric motion or the distinct motility per individual
object viewed. These are mostly hidden from the observer and manifest as often
highly non-linear transformations between consecutive video frames. Therefore,
video prediction is of interest not only in anticipating visual changes in the
real world but has, above all, emerged as an unsupervised learning rule
targeting the formation and dynamics of the observed environment. Many of the
deep learning-based state-of-the-art models for video prediction utilize some
form of recurrent layers like Long Short-Term Memory (LSTMs) or Gated Recurrent
Units (GRUs) at the core of their models. Although these models can predict the
future frames, they rely entirely on these recurrent structures to
simultaneously perform three distinct tasks: extracting transformations,
projecting them into the future, and transforming the current frame. In order
to completely interpret the formed internal representations, it is crucial to
disentangle these tasks. This paper proposes a fully differentiable building
block that can perform all of those tasks separately while maintaining
interpretability. We derive the relevant theoretical foundations and showcase
results on synthetic as well as real data. We demonstrate that our method is
readily extended to perform motion segmentation and account for the scene's
composition, and learns to produce reliable predictions in an entirely
interpretable manner by only observing unlabeled video data.
