3D Gaussian Splatting (3DGS) has become an emerging tool for dynamic scene
reconstruction. However, existing methods focus mainly on extending static 3DGS
into a time-variant representation, while overlooking the rich motion
information carried by 2D observations, thus suffering from performance
degradation and model redundancy. To address the above problem, we propose a
novel motion-aware enhancement framework for dynamic scene reconstruction,
which mines useful motion cues from optical flow to improve different paradigms
of dynamic 3DGS. Specifically, we first establish a correspondence between 3D
Gaussian movements and pixel-level flow. Then a novel flow augmentation method
is introduced with additional insights into uncertainty and loss collaboration.
Moreover, for the prevalent deformation-based paradigm that presents a harder
optimization problem, a transient-aware deformation auxiliary module is
proposed. We conduct extensive experiments on both multi-view and monocular
scenes to verify the merits of our work. Compared with the baselines, our
method shows significant superiority in both rendering quality and efficiency.

提出了一种新的运动感知增强框架，通过挖掘光流中的有用运动线索来改进动态场景重建，以提高不同动态 3D 高斯喷溅方式的性能，并针对基于形变的范例提出了一种瞬态感知形变辅助模块。经过广泛的实验证明，与基线相比，我们的方法在渲染质量和效率方面都表现出明显的优势。