  Authoring an appealing animation for a virtual character is a challenging
task. In computer-aided keyframe animation artists define the key poses of a
character by manipulating its underlying skeletons. To look plausible, a
character pose must respect many ill-defined constraints, and so the resulting
realism greatly depends on the animator's skill and knowledge. Animation
software provide tools to help in this matter, relying on various algorithms to
automatically enforce some of these constraints. The increasing availability of
motion capture data has raised interest in data-driven approaches to pose
design, with the potential of shifting more of the task of assessing realism
from the artist to the computer, and to provide easier access to nonexperts. In
this article, we propose such a method, relying on neural networks to
automatically learn the constraints from the data. We describe an efficient
tool for pose design, allowing na{\"i}ve users to intuitively manipulate a pose
to create character animations.
