TrajForward: Feed-Forward Dynamic 3D Gaussian Reconstruction for Urban Driving Scenes with Explicit Motion Trajectories

Reconstructing dynamic driving scenes with feed-forward 3D Gaussian Splatting remains fundamentally challenged by temporal inaccuracies and under-constrained entity-level correspondence, leading to limited dynamic expressiveness, unstable aggregation, and novel-view artifacts. These issues stem from the local or discrete low-order approximation and the lack of explicit entity-level constraint under self-supervision. To address this, we propose to derive target-time Gaussian centers in an entity-aware manner that not only captures coherent nonlinear motions but also preserves persistent temporal correspondence and intra-entity local variations. We materialize this concept through TrajForward, a feed-forward dynamic 3DGS framework that instantiates Gaussian centers along entity-conditioned trajectories. Specifically, to resolve entity-association ambiguity, we introduce learnable motion-bearing slots as latent proxies. These slots bind dynamically related Gaussians, providing explicit structural coupling and stable cross-time correspondence without object annotations. Building upon these proxies, we tackle heterogeneous nonlinear dynamics through a hierarchical trajectory representation. By decomposing motion into slot-level macroscopic trajectories and Gaussian-specific local residuals, this elegantly preserves both entity coherence and local flexibility. Finally, to prevent self-supervised trajectory instability, we formulate a suite of trajectory-aware objectives. By explicitly regularizing slot assignments and trajectory smoothness, these objectives mitigate static-dynamic entanglement and promote temporally coherent, artifact-reduced reconstructions. Experiments on challenging urban driving benchmarks validate TrajForward and demonstrate improved rendering fidelity. Code will be released.