  Manually authoring transition animations for a complete locomotion system can
be a tedious and time-consuming task, especially for large games that allow
complex and constrained locomotion movements, where the number of transitions
grows exponentially with the number of states. In this paper, we present a
novel approach, based on deep recurrent neural networks, to automatically
generate such transitions given a past context of a few frames and a target
character state to reach. We present the Recurrent Transition Network (RTN),
based on a modified version of the Long-Short-Term-Memory (LSTM) network,
designed specifically for transition generation and trained without any gait,
phase, contact or action labels. We further propose a simple yet principled way
to initialize the hidden states of the LSTM layer for a given sequence which
improves the performance and generalization to new motions. We both
quantitatively and qualitatively evaluate our system and show that making the
network terrain-aware by adding a local terrain representation to the input
yields better performance for rough-terrain navigation on long transitions. Our
system produces realistic and fluid transitions that rival the quality of
Motion Capture-based ground-truth motions, even before applying any
inverse-kinematics postprocess. Direct benefits of our approach could be to
accelerate the creation of transition variations for large coverage, or even to
entirely replace transition nodes in an animation graph. We further explore
applications of this model in a animation super-resolution setting where we
temporally decompress animations saved at 1 frame per second and show that the
network is able to reconstruct motions that are hard to distinguish from
un-compressed locomotion sequences.
