  We predict future video frames from complex dynamic scenes, using an
invertible neural network as the encoder of a nonlinear dynamic system with
latent linear state evolution. Our invertible linear embedding (ILE)
demonstrates successful learning, prediction and latent state inference. In
contrast to other approaches, ILE does not use any explicit reconstruction loss
or simplistic pixel-space assumptions. Instead, it leverages invertibility to
optimize the likelihood of image sequences exactly, albeit indirectly.
Comparison with a state-of-the-art method demonstrates the viability of our
approach.
