  We present PredRNN++, an improved recurrent network for video predictive
learning. In pursuit of a greater spatiotemporal modeling capability, our
approach increases the transition depth between adjacent states by leveraging a
novel recurrent unit, which is named Causal LSTM for re-organizing the spatial
and temporal memories in a cascaded mechanism. However, there is still a
dilemma in video predictive learning: increasingly deep-in-time models have
been designed for capturing complex variations, while introducing more
difficulties in the gradient back-propagation. To alleviate this undesirable
effect, we propose a Gradient Highway architecture, which provides alternative
shorter routes for gradient flows from outputs back to long-range inputs. This
architecture works seamlessly with causal LSTMs, enabling PredRNN++ to capture
short-term and long-term dependencies adaptively. We assess our model on both
synthetic and real video datasets, showing its ability to ease the vanishing
gradient problem and yield state-of-the-art prediction results even in a
difficult objects occlusion scenario.
