Motion-Aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction

3D Gaussian Splatting (3DGS) has become an emerging tool for dynamic scene reconstruction. However, existing methods mainly focus on developing various strategies to extend static 3DGS into a time-variant representation, while overlooking the rich motion information implicitly 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 step beyond the vanilla render-based cross-dimensional supervision that suffers from ambiguity and instability, and establish a more robust and effective dense correspondence between 3D Gaussian movements and pixel-level flows. Then a novel flow augmentation method is introduced with additional insights into uncertainty and loss collaboration. Furthermore, 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. The code will be publicly available at https://github.com/jasongzy/MAGS.