RealDrop: Predicting Realistic Impact Responses of Dropping

Single-image physical reconstruction aims to recover a 3D asset that not only matches appearance but also responds realistically to physical interactions. We focus on object dropping, a particularly revealing setting where impact can trigger diverse and coupled responses, including rebound, deformation, fracture, component motion, liquid release, and splash. However, existing physics-aware reconstruction methods often rely on deformation-dominated physical representations, causing materially complex objects to collapse to overly simple or physically incorrect impact behaviors. We present RealDrop, a framework for predicting realistic dropping responses from a single image. RealDrop constructs part-aware physical assets and introduces a Stress-Responsive Particle System (SRPS) that supports appropriate solid responses from elastic rebound to spatially coherent fracture, together with an SPH-based formulation for liquid dynamics. These representations allow distinct physical behaviors to coexist and interact within heterogeneous objects. We further introduce PhysLab, which turns physical-property inference from appearance-based prediction into consequence-driven verification by executing predicted properties through simulation and iteratively correcting those that produce physically inconsistent outcomes. Extensive experiments demonstrate that RealDrop produces substantially more realistic and expressive impact responses across diverse single-image inputs.