PhyMAGIC: Physical Motion-Aware Generative Inference with Confidence-guided VLM

Inferring physical properties from a single image is funda-mentally under-constrained. Attributes such as density, elasticity, andyield stress govern how objects move, yet they are largely invisible ina static frame. Existing physics-aware methods attempt to resolve thisambiguity through task-specific fine-tuning or supervised property es-timation, but both strategies struggle to generalize across diverse ma-terials and scenes. We observe that different motions expose comple-mentary physical cues. Building on this observation, we propose Phy-MAGIC, a training-free framework that actively probes physical prop-erties by synthesizing targeted motions from a single image. Specifically,PhyMAGIC uses a pretrained image-to-video model to construct mo-tion probes that generate diverse dynamic sequences from the inputimage. A vision-language model then analyzes these sequences to esti-mate physical parameters, each accompanied by a confidence score. Pa-rameters with low confidence trigger targeted prompt refinement, whichgenerates additional probe motions to gather complementary evidence.Once all parameters reach sufficient confidence, PhyMAGIC compilesthem into a complete physical specification and executes it in a differ-entiable Material Point Method simulator initialized from 3D Gaussianreconstructions. Experiments on diverse real-world scenes demonstratethat PhyMAGIC achieves stronger text-motion alignment and higherhuman-rated physical plausibility than state-of-the-art open-source videogenerators and physics-aware baselines. Code is available at: https://mengsiwei.github.io/MAGIC/.