  Human motion taxonomies serve as high-level hierarchical abstractions that
classify how humans move and interact with their environment. They have proven
useful to analyse grasps, manipulation skills, and whole-body support poses.
Despite substantial efforts devoted to design their hierarchy and underlying
categories, their use remains limited. This may be attributed to the lack of
computational models that fill the gap between the discrete hierarchical
structure of the taxonomy and the high-dimensional heterogeneous data
associated to its categories. To overcome this problem, we propose to model
taxonomy data via hyperbolic embeddings that capture the associated
hierarchical structure. We achieve this by formulating a novel Gaussian process
hyperbolic latent variable model that incorporates the taxonomy structure
through graph-based priors on the latent space and distance-preserving back
constraints. We validate our model on three different human motion taxonomies
to learn hyperbolic embeddings that faithfully preserve the original graph
structure. We show that our model properly encodes unseen data from existing or
new taxonomy categories, and outperforms its Euclidean and VAE-based
counterparts. Finally, through proof-of-concept experiments, we show that our
model may be used to generate realistic trajectories between the learned
embeddings.
