Beyond Continuous Deformation: State-Aware Hybrid Dynamics for 4D Gaussian Generation

Recent approaches to 4D generation represent dynamic objects using deformable 3D Gaussians whose transformations are predicted by continuous spatiotemporal fields. Such direct coordinate-time mappings, however, propagate no motion state between adjacent timestamps, and provide no mechanism for adapting how strongly each primitive contributes at a given timestamp. We propose State-Aware Hybrid Dynamics (SAHyD), which assigns each Gaussian a 3-D motion state in center-coordinate space and updates it through a hybrid rule combining a learned continuous drift with an expected latent-event response under soft motion regimes. The resulting regime and event features condition the continuous deformation representation before Gaussian attributes are decoded, so the state shapes the predicted offsets rather than correcting them afterwards. To regularize the learned transition across step sizes, Fine-Grained Motion Integration (FMint) learns a residual center-motion correction from a full-step versus two-half-step consistency reference. Time-Varying Gaussian Existence (TVGE) then predicts a soft participation probability that gates opacity and suppresses residual primitives, while preserving the number of Gaussians. Extensive experiments on video-to-4D generation demonstrate consistent improvements in temporal quality and overall reconstruction fidelity over the continuous-deformation backbone.