4DGS-JEPA proposes temporally compositional prediction for dynamic gaussian splatting
The authors introduce 4DGS-JEPA, a Gaussian‑native joint‑embedding predictive architecture aimed at multi‑horizon forecasting of dynamic Gaussian scenes. The model builds a hierarchical representation that spans the overall scene, motion groups, and individual Gaussians, and it employs a horizon‑conditioned transition operator that can generate both direct predictions and recursive rollouts.…
Key points
- 4DGS-JEPA uses hierarchical scene, motion-group, and Gaussian-level representations with a horizon‑conditioned transition operator.
- Temporal composition enforces that different transition paths to the same future endpoint yield compatible predictive states.
- Hybrid correspondence combines persistent canonical identity with residual optimal‑transport matching to handle reordering and topology changes.
A hybrid correspondence mechanism is also presented, merging a persistent canonical identity with residual optimal‑transport matching to cope with reordering and topology changes. The paper provides theoretical analysis of zero‑loss path agreement and bounded rollout error, and reports three controlled experiments showing that temporal composition reduces latent path dependence, geometry‑level composition improves motion consistency, and hybrid correspondence maintains reliable identity under ambiguous matches. The work positions 4DGS-JEPA as a predictive, temporally compositional formulation for dynamic Gaussian worlds.
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