  In this paper we present a new deep learning-driven approach to image-based
synthesis of animations involving humanoid characters. Unlike previous deep
approaches to image-based animation our method makes no assumptions on the type
of motion to be animated nor does it require dense temporal input to produce
motion. Instead we generate new animations by interpolating between user chosen
keyframes, arranged sparsely in time. Utilizing a novel configuration manifold
learning approach we interpolate suitable motions between these keyframes. In
contrast to previous methods, ours requires less data (animations can be
generated from a single youtube video) and is broadly applicable to a wide
range of motions including facial motion, whole body motion and even scenes
with multiple characters. These improvements serve to significantly reduce the
difficulty in producing image-based animations of humanoid characters, allowing
even broader audiences to express their creativity.
