HEIR: Learning Human-Entity Interactions with Functional Roles

Di Wen, Wenhao Guo, Yuedong Tan, Yun Huang, Minheng Wu +13

Understanding human-entity interactions requires recovering each person-action event's participants, roles, and shared identities. This structure can support embodied agents by clarifying who acts on which entities and how, informing anticipation and coordination in shared environments. Standard HOI metrics score individual links, leaving complete event composition undermeasured. We introduce HEIR (Human-Entity Interactions with Functional Roles), an image benchmark for complete grounded participant-role sets across object, interpersonal, and self-directed interactions. It contains 18,730 images, six roles, 105 actions, and 437 nouns, with shared entities, role changes, and repeated fillers; 51.6% of images contain multiple actors and 62.1% contain multiple actions. HEIR pairs relation AP with complete-set AP and structural evaluation. We also introduce CoRISP (Compositional Role-aware Interaction Set Prediction), which uses shared entity identities to combine role-conditioned evidence and predict normalized participant-role sets. Cardinality and role-multiplicity potentials couple assignments through event size and role composition, with exact per-event normalization. Across 16…