  In this work we present a novel, robust transition generation technique that
can serve as a new tool for 3D animators, based on adversarial recurrent neural
networks. The system synthesizes high-quality motions that use
temporally-sparse keyframes as animation constraints. This is reminiscent of
the job of in-betweening in traditional animation pipelines, in which an
animator draws motion frames between provided keyframes. We first show that a
state-of-the-art motion prediction model cannot be easily converted into a
robust transition generator when only adding conditioning information about
future keyframes. To solve this problem, we then propose two novel additive
embedding modifiers that are applied at each timestep to latent representations
encoded inside the network's architecture. One modifier is a time-to-arrival
embedding that allows variations of the transition length with a single model.
The other is a scheduled target noise vector that allows the system to be
robust to target distortions and to sample different transitions given fixed
keyframes. To qualitatively evaluate our method, we present a custom
MotionBuilder plugin that uses our trained model to perform in-betweening in
production scenarios. To quantitatively evaluate performance on transitions and
generalizations to longer time horizons, we present well-defined in-betweening
benchmarks on a subset of the widely used Human3.6M dataset and on LaFAN1, a
novel high quality motion capture dataset that is more appropriate for
transition generation. We are releasing this new dataset along with this work,
with accompanying code for reproducing our baseline results.
