KineticGS: Momentum-driven Coherent 4D Gaussian Splatting for Monocular Dynamic Scene Reconstruction

We present KineticGS, a novel approach for reconstructingdynamic scenes from monocular videos through momentum-driven 4DGaussian Splatting. While recent advances in motion modeling have im-proved dynamic reconstruction, they often produce temporal inconsis-tencies and non-physical deformations due to the lack of physical pri-ors constraining motion trajectories. KineticGS addresses this with aphysically grounded momentum hierarchy that models Gaussian parti-cles as momentum-carrying entities, thereby establishing a hierarchicalmotion representation from pixel observations to object-level coherence.Specifically, Gaussian particles serve as momentum carriers, with dy-namics driven by local motion energy. We perform momentum-guidedparticle sampling, concentrating representation in high-energy regionsto capture fine-grained non-rigid motions. A learnable energy-flow mod-ule then predicts per-particle temporal activation, ensuring continuousand physically plausible motion propagation. Further, a holistic kineticsynchronization mechanism enforces consistent motion evolution amongcorrelated particles, preserving structural integrity without explicit disin-tegration. Experiments on dynamic scene datasets show that KineticGSimproves reconstruction fidelity, temporal coherence, and physical real-ism, achieving +0.91 dB average PSNR gain in dynamic-object regionsover the second-best.