SiPhy: Single-Image Physical Property Reasoning

Inferring physical properties such as mass, stiffness, and elas-ticity from a single image is essential for simulation and embodied AI, yetmost existing approaches rely on multi-view reconstruction or physics-based supervision. We introduce SiPhy, a unified framework for single-image physical property reasoning that aligns 3D-aware visual cues, depthwith language-based material knowledge. From one RGB image, SiPhysamples pseudo-voxel points, extracts CLIP features, and grounds themto material candidates proposed by an VLM. A part-based contrastiveaggregator enforces region consistency, while a heaviness-aware refine-ment improves thickness and volume estimation for dense objects.Across ABO-500, MVImgNet-100, and PhysXNet-100, SiPhy achievesstate-of-the-art single-image performance, surpassing multi-view recon-struction methods by improving mass MnRE by up to 93% (vs. PUGS),reducing density MAE by 35.5% (vs. NeRF2Physics), and loweringYoung’s modulus error by 23.5%. We further validate SiPhy on realhand–object interaction datasets, demonstrating its potential as a dataannotation engine for physical understanding from single-view imagery.Codes are available at https://github.com/DominoAI-Lab/SiPhy-ECCV-2026.