GaussTrace: Persistent 3D Trajectories for Dynamic Gaussian Reconstruction from Monocular Video

Reconstructing dynamic 3D scenes from casually captured monocular video requires temporally coherent motion under sparse, ambiguous observations, yet existing 4D Gaussian Splatting (4DGS) pipelines rely on fragmented preprocessing or expensive scene-specific optimization. We present **GaussTrace**, a framework built around persistent 3D trajectories that converts a single feed-forward 4D prediction into unified priors for dynamic Gaussian reconstruction. *Trajectory-Basis Motion Modeling* (TBMM) groups similar trajectory bases and learns shared translation corrections with local residuals, while *Motion-Aware Routing and Temporal Coreset* (MRTC) combines tri-state motion routing with motion-aware time selection to allocate motion freedom and focus refinement on representative observations. *Progressive Gaussian Optimization* (PGO) separates scene stabilization from motion refinement before jointly optimizing Gaussian attributes and motion corrections. Experiments on NVIDIA Dynamic Scenes, iPhone DyCheck, and DAVIS show strong reconstruction quality, sharper dynamic boundaries, and substantially more efficient optimization than representative monocular 4DGS systems.