MVFusion-GS: Motion-Variance Guided Temporal Attention for High-Quality Dynamic Gaussian Splatting

3D Gaussian Splatting (3DGS) enables real-time novel viewsynthesis for static scenes. Extending it to dynamic scenes via deforma-tion fields has recently attracted significant attention, particularly fordynamic scene reconstruction and distractor-free reconstruction. How-ever, existing deformation networks lack explicit motion awareness: theyneither capture long-term motion intensity nor exploit short-term tempo-ral coherence, leading to inaccurate foreground deformation and pseudo-static residuals in the background. We present MVFusion-GS, a methodthat enhances deformation networks with two complementary motion-aware mechanisms. The Motion-Variance Guided Refinement ag-gregates per-Gaussian deformation statistics across time to estimate mo-tion variance and uses it to guide dynamic-static separation during defor-mation prediction. The MotionFormer Temporal Attention moduleapplies Transformer self-attention over neighboring timesteps to modellocal motion dependencies and improve temporal consistency. Extensiveexperiments on both dynamic scene reconstruction and distractor-free re-construction benchmarks demonstrate state-of-the-art performance, show-ing that explicit motion awareness improves both foreground motionmodeling and static background reconstruction.