HyperCAPE: Learning Higher-Order Keypoint Structures for Category-Agnostic Pose Estimation

Category-Agnostic Pose Estimation (CAPE) aims to localize semantic keypoints on unseen object categories. Recent CAPE methods increasingly exploit structural relations among keypoints, yet existing formulations predominantly rely on pairwise graphs, which do not explicitly represent group-wise structures jointly formed by multiple semantic keypoints. We propose HyperCape, a higher-order keypoint modeling framework that induces a latent hypergraph from contextualized keypoint representations. HyperCape sparsely assigns each keypoint to a subset of latent hyperedges, allowing related keypoints to form shared structural groups while permitting each keypoint to participate in multiple relations. The learned hypergraph not only enables higher-order feature interactions, but also directly guides coordinate refinement by modeling the relative geometry of keypoints within each hyperedge. Experiments on the MP-100 benchmark show that HyperCape outperforms existing graph-based CAPE methods, achieving gains of $1.04$%p in mPCK and $2.05$%p at [email protected] over the strongest prior method. Qualitative analyses further show that the learned hyperedges form coherent higher-order groups of structurally related keypoints across unseen categories. These results highlight the benefit of learned higher-order structure for accurate category-agnostic keypoint localization.