SplineGS: Robust Motion-Adaptive Spline for Real-Time Dynamic 3D
  Gaussians from Monocular Video
Authors: name: Jongmin Park, name: Minh-Quan Viet Bui, name: Juan Luis Gonzalez Bello, name: Jaeho Moon, name: Jihyong Oh, name: Munchurl Kim

Synthesizing novel views from in-the-wild monocular videos is challenging due
to scene dynamics and the lack of multi-view cues. To address this, we propose
SplineGS, a COLMAP-free dynamic 3D Gaussian Splatting (3DGS) framework for
high-quality reconstruction and fast rendering from monocular videos. At its
core is a novel Motion-Adaptive Spline (MAS) method, which represents
continuous dynamic 3D Gaussian trajectories using cubic Hermite splines with a
small number of control points. For MAS, we introduce a Motion-Adaptive Control
points Pruning (MACP) method to model the deformation of each dynamic 3D
Gaussian across varying motions, progressively pruning control points while
maintaining dynamic modeling integrity. Additionally, we present a joint
optimization strategy for camera parameter estimation and 3D Gaussian
attributes, leveraging photometric and geometric consistency. This eliminates
the need for Structure-from-Motion preprocessing and enhances SplineGS's
robustness in real-world conditions. Experiments show that SplineGS
significantly outperforms state-of-the-art methods in novel view synthesis
quality for dynamic scenes from monocular videos, achieving thousands times
faster rendering speed.