Forecasting dynamic scenes remains a fundamental challenge in computer vision, as limited observations make it difficult to capture coherent object-level motion and long-term temporal evolution. We present Motion Group-aware Gaussian Forecasting (MoGaF), a framework for long-term scene extrapolation built upon the 4D Gaussian Splatting representation. MoGaF introduces motion-aware Gaussian grouping and group-wise optimization to enforce physically consistent motion across both rigid and non-rigid regions, yielding spatially coherent dynamic representations. Leveraging this structured space-time representation, a lightweight forecasting module predicts future motion, enabling realistic and temporally stable scene evolution. Experiments on synthetic and real-world datasets demonstrate that MoGaF consistently outperforms existing baselines in rendering quality, motion plausibility, and long-term forecasting stability. Our project page is available at https://slime0519.github.io/mogaf

翻译：动态场景预测仍然是计算机视觉领域的一项基础性挑战，由于观测数据有限，难以捕捉连贯的物体级运动与长期时间演化规律。本文提出运动感知高斯预测框架（MoGaF），这是一种基于4D高斯溅射表示的长时期场景外推框架。MoGaF通过引入运动感知高斯分组与分组优化机制，在刚性与非刚性区域均强制保持物理一致的运动特性，从而生成空间连贯的动态表示。基于这种结构化的时空表示，轻量级预测模块能够对未来运动进行预测，实现逼真且时间稳定的场景演化。在合成数据集与真实数据集上的实验表明，MoGaF在渲染质量、运动合理性与长期预测稳定性方面均持续优于现有基线方法。项目页面详见 https://slime0519.github.io/mogaf