Space-Time Forecasting of Dynamic Scenes with Motion-aware Gaussian Grouping

• URL: http://arxiv.org/abs/2602.21668v1
• Date: Wed, 25 Feb 2026 08:04:07 GMT
• Title: Space-Time Forecasting of Dynamic Scenes with Motion-aware Gaussian Grouping
• Abstract summary: MoGaF is a framework for long-term scene extrapolation built upon the 4D Gaussian Splatting representation.<n>A lightweight forecasting module predicts future motion, enabling realistic and temporally stable scene evolution.<n>Experiments on synthetic and real-world datasets demonstrate that MoGaF consistently outperforms existing baselines in rendering quality, motion plausibility, and long-term forecasting stability.
• Score: 47.282424020983996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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