FutureGS: Structured Gaussian Fields for Future-Aware Dynamic Scene Modeling

摘要

Recent advances in 4D Gaussian Splatting have boosted dynamic scene reconstruction and real-time rendering. However, current methods remain retrospective, lacking the ability to forecast future states-limiting their utility in tasks like autonomous navigation and robotics. To address these limitations, we propose FutureGS, a novel Gaussian-based dynamic scene representation framework tailored for continuous 3D future scene prediction and view synthesis. FutureGS introduces a dual-domain decoupled representation, consisting of a static 3D Gaussian base to maintain spatial consistency and a dynamic deformation field to explicitly model temporal motion evolution. To capture long-range dependencies and complex motion dynamics, we design a multi-window collaborative prediction strategy that leverages a sliding temporal window and a bidirectional LSTM-based temporal encoder for robust future motion estimation. Furthermore, we propose a KNN-based local rigidity-aware fusion mechanism, which adaptively regulates the prediction consistency based on local deformation intensity, enhancing the geometric stability and physical plausibility of future scenes. Extensive experiments on standard dynamic scene benchmarks, including D-NeRF and NeRF-DS, demonstrate that FutureGS achieves superior performance in terms of visual fidelity and spatiotemporal consistency, enabling real-time and photorealistic rendering from arbitrary viewpoints at future time steps.