BUMP: Body-driven Unified Multi-object Physics for HOI synthesis

Generating human-object interactions (HOIs) with multiple objects is challenging because the resulting motions arise from different underlying causes. Human motion reflects semantic intent, intentionally manipulated objects must remain coordinated with the human, while many surrounding objects move primarily as physical consequences of contact and collision. We present BUMP, a text-driven framework for multi-object HOI generation based on a simple principle: learn intentional motion and simulate physical consequences. Our method first generates full-body human motion from the text and scene context, then predicts the trajectories of a small number of intentionally manipulated driver objects conditioned on the human motion. Rather than explicitly generating every moving object, we obtain the motions of physically induced responder objects through simulation, allowing the framework to handle varying numbers and configurations of surrounding objects without increasing the learned trajectory output space. To address the scarcity of paired full-body multi-object HOI data, we further combine heterogeneous supervision from human-only motion, full-body HOI, and hand-centric manipulation data augmented with plausible body motion. Finally, we introduce outcome-conditioned interaction editing, which infers a minimal modification of the causal driver trajectory from a desired responder behavior without requiring an explicit target driver trajectory. Extensive experiments demonstrate that BUMP outperforms baseline methods on both single and multiple object interaction generation. We further showcase diverse scenarios where inter-object relationships are crucial (\eg, stacking box), highlighting the advantage of our unified model formulation.