  Our work focuses on the development of a learnable neural representation of
human pose for advanced AI assisted animation tooling. Specifically, we tackle
the problem of constructing a full static human pose based on sparse and
variable user inputs (e.g. locations and/or orientations of a subset of body
joints). To solve this problem, we propose a novel neural architecture that
combines residual connections with prototype encoding of a partially specified
pose to create a new complete pose from the learned latent space. We show that
our architecture outperforms a baseline based on Transformer, both in terms of
accuracy and computational efficiency. Additionally, we develop a user
interface to integrate our neural model in Unity, a real-time 3D development
platform. Furthermore, we introduce two new datasets representing the static
human pose modeling problem, based on high-quality human motion capture data,
which will be released publicly along with model code.
