How to train your dog: Neural enhancement of quadruped animations

Creating realistic quadruped animations is challenging. Producing
realistic animations using methods such as key-framing is time consuming and requires much artistic expertise. Alternatively, motion
capture methods have their own challenges (getting the animal into
a studio, attaching motion capture markers, and getting the animal
to put on the desired performance) and the resulting animation
will still most likely require cleaning up. It would be useful if an
animator could provide an initial rough animation and in return be
given a corresponding high quality realistic one. To this end, we
present a deep-learning approach for the automatic enhancement of
quadruped animations. Given an initial animation, possibly lacking
the subtle details of true quadruped motion and/or containing small
errors, our results show that it is possible for a neural network to learn how to add these subtleties and correct errors to produce an
enhanced animation while preserving the semantics and context
of the initial animation. Our work also has potential uses in other
applications, for example, its ability to be used in real-time means
it could form part of a quadruped embodiment system.