SourcePhys: Source-Conditional Physical Completion for Simulation-Ready 3D Assets

Large 3D collections contain assets in very different states of physical readiness. Generated meshes may lack physical structure, while native simulation assets may already provide bodies, joints, and valid parameters. We present SourcePhys, a framework for source-conditional physical completion that preserves available evidence and infers unresolved attributes. It selects a part representation from the source, combines constrained vision-language inference with material priors and component geometry, and arbitrates direct and geometry-based mass estimates when they disagree. The completed physical records retain the origin of inferred values and can be exported to OpenUSD for simulation. We evaluate 775 assets from four source cohorts. On 150 randomly sampled Amazon Berkeley Objects, the full method reaches 43.6% median mass error; 743 assets have an original or repaired variant that passes cohort-specific OpenUSD and Isaac Sim checks. Controlled behavior probes and robot interactions further illustrate how the completed assets enter interactive workflows. These results offer a practical route from heterogeneous 3D content to physically grounded assets for embodied AI.