FS-GStream: Distribution-Adaptive Motion Masking and Optimization for Flicker-Suppressed 3D Gaussian Streaming

The online reconstruction of dynamic scenes from multi-view video streams enables fast free-viewpoint video synthesis, but existing streaming 3DGS methods often suffer from temporal flickering with geometry degradation. Temporal flickering partly arises from the difficulty of distinguishing true scene motion from static background during per-frame optimization, leading to unnecessary changes in static scene representations even when they do not change. In this paper, we propose FS-GStream, distribution-adaptive motion masking and optimization for flicker-suppressed 3D Gaussian streaming, which can be easily integrated as a generalizable plug-and-play module with two-stage streaming 3D Gaussian reconstruction models. Our FS-GStream significantly improves temporal consistency through two main components: Distribution-Adaptive Motion Masking (DAMM) and Mask-Guided Optimization (MGO). The DAMM first estimates a frame-wise motion threshold from the estimated distribution of log-transformed Gaussian translation magnitudes, which stably separates static and dynamic Gaussians in a statistical manner without manually setting a fixed displacement threshold. Guided by the masks that distinguish static and dynamic Gaussians from the DAMM, the MGO jointly refines the geometry and appearance of dynamic Gaussians while preserving static geometry and selectively adapting static appearance, and restricts densification and pruning to dynamic Gaussians. Extensive experiments on state-of-the-art two-stage streaming baselines demonstrate the generalizability of our plug-and-play design, which consistently alleviates temporal flickering and significantly improves qualitative and quantitative performance in highly dynamic scenes}.