  Generating animations from natural language sentences finds its applications
in a a number of domains such as movie script visualization, virtual human
animation and, robot motion planning. These sentences can describe different
kinds of actions, speeds and direction of these actions, and possibly a target
destination. The core modeling challenge in this language-to-pose application
is how to map linguistic concepts to motion animations.
  In this paper, we address this multimodal problem by introducing a neural
architecture called Joint Language to Pose (or JL2P), which learns a joint
embedding of language and pose. This joint embedding space is learned
end-to-end using a curriculum learning approach which emphasizes shorter and
easier sequences first before moving to longer and harder ones. We evaluate our
proposed model on a publicly available corpus of 3D pose data and
human-annotated sentences. Both objective metrics and human judgment evaluation
confirm that our proposed approach is able to generate more accurate animations
and are deemed visually more representative by humans than other data driven
approaches.
