After Training: Targeted Post-Training Refinement for 4D Gaussian Splatting

Refining a trained dynamic scene requires correcting residual errors without disrupting what has already been reconstructed well. This is challenging in deformation-based four-dimensional Gaussian Splatting (4DGS), where canonical updates affect multiple time steps and newly added Gaussians interact with existing ones through compositing. We propose *targeted post-training refinement*, which controls refinement along both temporal and spatial dimensions. Temporally, additional Gaussians reuse the learned deformation field while learning time-specific corrections for residual errors. Spatially, refinement starts from the source Gaussians used to initialize the new Gaussians and progressively expands to the remaining representation, avoiding unnecessary global updates at the outset. Across two deformation-based 4DGS models and three dynamic-scene datasets, our method consistently improves the input models and outperforms continued-training baselines. Further analysis shows that time-specific correction primarily increases the correction of remaining errors, while restricting the initial refinement scope reduces damage to already well-reconstructed regions.