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, can be used to generate realistic trajectories between the embeddings, and outperforms its Euclidean and VAE-based counterparts.

翻译：人类运动分类法作为高层级层次化抽象，用于对人类运动及与环境交互的方式进行归类。这类分类法在分析抓取动作、操作技能以及全身支撑姿态中已被证明具有实用价值。尽管已有大量研究致力于设计分类法的层级结构及其底层类别，但其应用仍然有限。这可能归因于缺乏能够弥合分类法离散层级结构与对应类别高维异构数据之间差距的计算模型。为解决这一问题，我们提出通过捕获层级结构的双曲嵌入对分类法数据进行建模。具体而言，我们构建了一种新型高斯过程双曲潜变量模型，该模型通过基于图的潜空间先验和保距反向约束来整合分类法结构。我们在三种不同的人类运动分类法上验证了模型，学习到的双曲嵌入能够忠实保留原始图结构。结果表明，我们的模型能正确编码来自现有或新分类类别的未知数据，可用于生成嵌入之间的逼真运动轨迹，并且其性能优于欧几里得空间及基于VAE的对应模型。