  The ability to generate complex and realistic human body animations at scale,
while following specific artistic constraints, has been a fundamental goal for
the game and animation industry for decades. Popular techniques include
key-framing, physics-based simulation, and database methods via motion graphs.
Recently, motion generators based on deep learning have been introduced.
Although these learning models can automatically generate highly intricate
stylized motions of arbitrary length, they still lack user control. To this
end, we introduce the problem of long-term inbetweening, which involves
automatically synthesizing complex motions over a long time interval given very
sparse keyframes by users. We identify a number of challenges related to this
problem, including maintaining biomechanical and keyframe constraints,
preserving natural motions, and designing the entire motion sequence
holistically while considering all constraints. We introduce a biomechanically
constrained generative adversarial network that performs long-term inbetweening
of human motions, conditioned on keyframe constraints. This network uses a
novel two-stage approach where it first predicts local motion in the form of
joint angles, and then predicts global motion, i.e. the global path that the
character follows. Since there are typically a number of possible motions that
could satisfy the given user constraints, we also enable our network to
generate a variety of outputs with a scheme that we call Motion DNA. This
approach allows the user to manipulate and influence the output content by
feeding seed motions (DNA) to the network. Trained with 79 classes of captured
motion data, our network performs robustly on a variety of highly complex
motion styles.
