HOI-UP: Adapting Human-Object Interaction Motion to Unseen Object Properties without Property-Labeled Motion Data

How humans interact with an object depends on its physical properties, yet widely used Human-Object Interaction (HOI) datasets provide little explicit supervision for property-dependent adaptation. We aim to adapt an existing HOI reference to unseen object properties without property-labeled motion data. Existing property-aware methods such as FORCE and PA-HOI require additional motion data for new property conditions, while motion editing approaches such as CoMo rely on discrete pose-code editing that compromises the precise contacts required for physical HOI execution. Biomechanical objectives, used as the sole adaptation guidance in motion optimization or physics-based imitation, yield similar motion strategies across object-property levels rather than human-like variations. In this paper, we introduce HOI-UP, a framework for adapting existing HOI references to unseen object properties. A vision-language model (VLM) predicts property-dependent changes to compact, literature-informed and empirically validated HOI-codes. Kinematic optimization adapts the reference toward these targets in an anatomically plausible HOI motion space. RL-based tracking then refines the resulting plans in physics simulation for execution under the target properties. We further introduce an evaluation protocol that jointly assesses property response, reference preservation, naturalness, biomechanical demand, and physical executability across object property changes. Our motions adapted to unseen properties follow human-like posture changes and vary monotonically along the property axis, while achieving the highest tracking success rate and the lowest tracking errors under constrained actuator powers.