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.
从自然场景的单目视频合成新视图是一个具有挑战性的问题，主要原因在于场景动态性和缺乏多视角信息。为了解决这一问题，我们提出了 SplineGS，一种无需 COLMAP 的动态三维高斯点云（3DGS）框架，能够从单目视频中实现高质量重建和快速渲染。该框架的核心是一个新颖的 运动自适应样条（Motion-Adaptive Spline, MAS） 方法，通过使用带少量控制点的三次 Hermite 样条来表示连续的动态三维高斯轨迹。 针对 MAS，我们设计了一种 运动自适应控制点修剪（Motion-Adaptive Control points Pruning, MACP） 方法，用于在不同运动情况下建模动态三维高斯的形变，同时逐步修剪控制点以保持动态建模的完整性。此外，我们提出了一种联合优化策略，通过光度一致性和几何一致性对相机参数和三维高斯属性进行联合优化。这种策略避免了对基于 Structure-from-Motion 的预处理需求，并增强了 SplineGS 在真实场景条件下的鲁棒性。 实验结果表明，SplineGS 在动态场景的单目视频新视图合成质量上显著优于现有方法，同时实现了数千倍的渲染速度提升。