Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene
  Reconstruction
Zhiyang Guo Wengang Zhou Li Li Min Wang Houqiang Li
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.