TRiGS: Temporal Rigid-Body Motion for Scalable 4D Gaussian Splatting

Recent 4D Gaussian Splatting (4DGS) methods achieve im-pressive dynamic scene reconstruction but often rely on piecewise linearvelocity approximations and short temporal windows. This disjointedmodeling leads to severe temporal fragmentation, forcing primitives tobe repeatedly eliminated and regenerated to track complex nonlinear dy-namics. This makeshift approximation eliminates the long-term temporalidentity of objects and causes an inevitable proliferation of Gaussians,hindering scalability to extended video sequences. To address this, wepropose TRiGS, a novel 4D representation that utilizes unified, contin-uous geometric transformations. By integrating SE(3) transformations,hierarchical Bézier residuals, and learnable local anchors, TRiGS mod-els geometrically consistent rigid motions for individual primitives. Thiscontinuous formulation preserves temporal identity and effectively mit-igates unbounded memory growth. Extensive experiments demonstratethat TRiGS achieves high fidelity rendering on standard benchmarkswhile uniquely scaling to extended video sequences (e.g., 600 to 1200frames) without severe memory bottlenecks, significantly outperformingprior works in temporal stability.