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.

翻译：从野外单目视频合成新视角因场景动态性和缺乏多视角线索而具有挑战性。为此，我们提出SplineGS，一种无需COLMAP的动态3D高斯泼溅（3DGS）框架，用于从单目视频实现高质量重建与快速渲染。其核心是一种新颖的运动自适应样条（MAS）方法，该方法使用具有少量控制点的三次Hermite样条来表示连续的动态3D高斯轨迹。对于MAS，我们引入了一种运动自适应控制点剪枝（MACP）方法，以建模每个动态3D高斯在不同运动下的形变，在保持动态建模完整性的同时逐步剪枝控制点。此外，我们提出了一种相机参数估计与3D高斯属性联合优化策略，利用光度一致性和几何一致性。这消除了对运动恢复结构预处理的需求，并增强了SplineGS在真实世界条件下的鲁棒性。实验表明，对于单目视频的动态场景新视角合成质量，SplineGS显著优于现有最先进方法，并实现了数千倍的渲染速度提升。