AutoPhyX: Automatic Text-Condition Physics Property Generation

High-fidelity 4D physical simulation requires accurate phys-ical parameter assignments that align with diverse material properties.Previous methods struggle with critical bottlenecks: they either rely oncomputationally intensive per-scene optimization or predict parametersfrom purely visual inputs. The latter suffers from inherent ambiguity,e.g., the inability to distinguish the stiffness of rubber from its ap-pearance alone. To bridge this gap, we propose AutoPhyX, a text-conditioned framework for predicting spatially-varying physical proper-ties. Since training robust feed-forward models requires fine-grained su-pervision, we first introduce a novel part-controllable data generationpipeline that decomposes complex 3D assets into semantically distinctcomponents and pairs them with physically plausible, diverse parame-ters and corresponding text descriptions. Leveraging this dataset, Auto-PhyX employs a cross-modal modulation mechanism where text featuresdynamically modulate visual features for precise physical grounding. Byformulating property prediction within a voxel field, AutoPhyX en-sures compatibility with diverse 3D representations, including meshes,point clouds, Gaussian Splatting, and NeRF. Experiments demonstratethat our method enables physically plausible, text-driven parameter as-signment in a single forward pass. It achieves high accuracy and robustgeneralization to in-the-wild objects, paving the way for future physics-intensive embodied and robotic applications.