SAGE-GS: Spatially Weighted Event Supervision and Sparse Temporal Gating for Dynamic Gaussian Splatting

Event observations provide inter-frame brightness-change constraints for dynamic scene reconstruction, but their spatial distribution does not necessarily reflect where supervision is most useful. In addition to geometric deformation, dynamic Gaussians need to modulate their rendering contributions over time to accommodate both persistent and temporally localized contributions. We propose SAGE-GS, which improves event-assisted dynamic Gaussian reconstruction through event supervision allocation and temporal representation and optimization. First, we use CTA to fuse current RGB and event features, incorporating dynamic-region priors, soft event-density weights, and amplitude-based confidence to predict spatial weights for event residuals. Second, we equip each dynamic Gaussian with a learnable temporal center, a fixed-scale temporal kernel, and an individual gating strength. Zero initialization, interval projection, and sparsity regularization enable selective temporal suppression while allowing Gaussians that do not require suppression to retain their original opacity. Both branches are jointly trained using RGB and weighted event reconstruction objectives, while the deformation network models geometric motion. Experiments on eight dynamic scenes show that SAGE-GS improves average PSNR by approximately 0.44 dB over E-D3DGS.