Temporally Aware Densification for Dynamic 3D Gaussian Splatting

Despite modeling temporal motion, dynamic 3D GaussianSplatting (3DGS) methods still inherit a static densification strategy ill-suited for dynamic scenes. This neglect of temporal behavior leads tounder-reconstructed and blurry dynamic regions, as short-lived Gaus-sians receive sparse supervision and fail to densify effectively. We pro-pose a Visibility-Aware Densification (VAD) framework that integratestemporal visibility into the densification process, ensuring that Gaus-sians are refined based on their actual temporal presence. A Temporally-Adaptive Thresholding (TAT) mechanism further adjusts each Gaussian’sdensification threshold according to its temporal lifespan, promoting bal-anced refinement of both static and dynamic regions. Finally, a TemporalOffset Warping (TOW) design enhances deformation capacity aroundtemporal centers, extending the lifespan of highly dynamic Gaussiansand facilitating more effective densification. Our approach achieves sub-stantial improvements in the visual quality of dynamic regions, outper-forming existing methods across three dynamic multi-view benchmarkdatasets. Moreover, the proposed VAD module generalizes across diversedynamic 3DGS methods, consistently improving dynamic reconstructionas a plug-and-play component. The project page is available here.