Physical-Field-Bridged Material Parameter Inference from Multi-View Deformation Videos

Estimating simulation-ready material parameters from visual observations is crucial for deformable-object manipulation, yet remains challenging because material properties are only indirectly expressed through physical responses such as contact, deformation, recovery, and stress concentration. We introduce physical-field-bridged material inference, a single-pass framework that treats dense physical responses as intermediate evidence between RGB deformation videos and a discrete constitutive type together with continuous material parameters. This design addresses a practical limitation of existing inverse-physics methods, which often rely on per-scene reconstruction, online tuning, or simulation-in-the-loop optimization, while feed-forward visual estimators can exploit appearance or category shortcuts. Given multi-view RGB videos, our model predicts force/contact masks, projected motion-flow fields, and projected stress-response maps, then conditions constitutive-type classification and parameter regression on these fields. To train and evaluate this formulation, we construct DeformFieldBench, a 3DGS–MPM learning environment with 5,000 simulations over 100 object shapes, five mechanically informative loading modes, and dense pixel-aligned physical supervision. Our method achieves the best median errors on all four continuous parameters, raises stress-hotspot recall from 0.0762 to 0.5121, and reaches 0.8676 SSIM/36.82 dB PSNR in same-simulator replay on unseen objects. Under a protocol-matched GSO evaluation, it performs inference and future-state prediction in approximately 30 seconds rather than the 15–120 minutes required by per-scene optimization; it also produces measurable replay agreement on five real-world sequences. These results support dense physical fields as an effective bridge for fast material inference, while cross-simulator and real-world tests expose remaining domain and identifiability gaps. The dataset is anonymously released at https://www.kaggle.com/datasets/anonymous336/physical-field-material-parameter-5000