ReLo-S4DGS: Reliability Modeling for Locality Optimization in Sparse-View Dynamic Gaussian Splatting

Recent 4D Gaussian Splatting methods have achieved impressive quality and efficiency for dynamic scene reconstruction, yet sparse-view inputs severely underconstrain scene geometry, leading to geometric ambiguity and view-inconsistent reconstructions. We observe substantial locality degradation in anchor-based Gaussian representations under sparse supervision: to fit limited observations or compensate for insufficient spatial coverage of anchors, anchors tend to decode excessively large offsets, placing Gaussians far from their source anchors and undermining representation locality. Based on this observation, we propose ReLo-S4DGS, which, to the best of our knowledge, is the first anchor-based Gaussian Splatting framework for sparse-view dynamic scene reconstruction. To address locality degradation, ReLo-S4DGS models anchor reliability and suppresses low-reliability rendering contributions to favor reliable local representations, we further introduce dynamic-aware locality correction to rectify excessive offsets and geometric regularization to constrain local geometry, thereby mitigating sparse-view overfitting. Extensive experiments on standard dynamic scene benchmarks demonstrate state-of-the-art performance of our method.