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.

翻译：人体运动分类法作为高层级的抽象分类体系，用于描述人类如何运动及与环境交互。这类分类法已被证明在分析抓取动作、操作技能及全身支撑姿态等方面具有重要价值。尽管学界已投入大量精力设计其层级结构与底层类别，但其实际应用仍受限。这可能归因于缺乏能够填补分类法离散层级结构与对应类别的高维异构数据之间鸿沟的计算模型。为解决此问题，我们提出通过双曲嵌入对分类法数据进行建模，以捕捉其层级结构特征。我们通过构建新型高斯过程双曲隐变量模型实现这一目标，该模型通过隐空间的图结构先验与距离保持反向约束来整合分类法结构。我们在三种不同的人体运动分类数据集上验证了模型的有效性，所学习的双曲嵌入能准确保持原始图结构。实验表明，该模型能正确编码来自现有或新增分类类别的新数据，其性能优于基于欧几里得空间和变分自编码器的对比模型。最后，通过概念验证实验，我们证明该模型可用于在已学习的嵌入之间生成符合真实运动规律的轨迹。