DePhy: Decoupled Physics-Aware Predictive Models for State Estimation, Forecasting and Data Assimilation

Physics-aware state estimation from noisy observations requires both robust measurement representations and dynamics that support physically plausible reconstruction. Existing physics-aware methods typically rely on reconstruction-driven representation learning, with physical consistency introduced through additional regularization or post-hoc correction. As a result, measurement-related nuisance variation can remain entangled with the states used for physical propagation. We introduce DePhy, a decoupled latent state-space framework that learns physical state propagation from measurement latent without reconstruction supervision. DePhy uses mask-based self-supervised learning to obtain representations that are robust to corrupted or incomplete measurements, while physics-mediated future latent prediction preserves information needed for physical state evolution. The resulting representations are mapped to a physical state space governed by an augmented physics-based model that combines analytical mechanics with bounded learned residuals. Amortized inference of trajectory-specific physical factors further enables instance-specific adaptation without per-instance optimization. To reconcile physics-based predictions with new observations, a Kalman-inspired neural assimilation module fuses the propagated state prior with current measurement evidence, yielding measurement-consistent states for reconstruction. By decoupling predictive representation and dynamics learning from reconstruction, DePhy learns states that are both robust to noisy measurements and suitable for stable physical propagation. Experiments on monocular human motion benchmarks demonstrate improved state estimation and long-horizon forecasting, with DePhy reducing Human3.6M MPJPE from 54.8 to 47.3 mm and acceleration error from 8.4 to 3.9 mm/s${}^2$ relative to OSDCap, while improving 1-s forecasting MPJPE from 103.9 to 90.8 mm relative to PhysMoP.