  Human character animation is often critical in entertainment content
production, including video games, virtual reality or fiction films. To this
end, deep neural networks drive most recent advances through deep learning and
deep reinforcement learning. In this article, we propose a comprehensive survey
on the state-of-the-art approaches based on either deep learning or deep
reinforcement learning in skeleton-based human character animation. First, we
introduce motion data representations, most common human motion datasets and
how basic deep models can be enhanced to foster learning of spatial and
temporal patterns in motion data. Second, we cover state-of-the-art approaches
divided into three large families of applications in human animation pipelines:
motion synthesis, character control and motion editing. Finally, we discuss the
limitations of the current state-of-the-art methods based on deep learning
and/or deep reinforcement learning in skeletal human character animation and
possible directions of future research to alleviate current limitations and
meet animators' needs.
