Dynamic view synthesis (DVS) has advanced remarkably in recent years, achieving high-fidelity rendering while reducing computational costs. Despite the progress, optimizing dynamic neural fields from casual videos remains challenging, as these videos do not provide direct 3D information, such as camera trajectories or the underlying scene geometry. In this work, we present RoDyGS, an optimization pipeline for dynamic Gaussian Splatting from casual videos. It effectively learns motion and underlying geometry of scenes by separating dynamic and static primitives, and ensures that the learned motion and geometry are physically plausible by incorporating motion and geometric regularization terms. We also introduce a comprehensive benchmark, Kubric-MRig, that provides extensive camera and object motion along with simultaneous multi-view captures, features that are absent in previous benchmarks. Experimental results demonstrate that the proposed method significantly outperforms previous pose-free dynamic neural fields and achieves competitive rendering quality compared to existing pose-free static neural fields. The code and data are publicly available at https://rodygs.github.io/.

本研究解决了从休闲视频中提取动态神经场面临的挑战，尤其是在缺乏直接3D信息的情况下。提出的RoDyGS优化管道通过分离动态和静态元件来有效学习场景的运动和几何，同时确保学习到的内容在物理上合理。此外，创建了新的基准测试Kubric-MRig，以提供更丰富的相机和物体运动数据。实验结果表明，该方法在渲染质量上优于先前的无姿态动态神经场。

RoDyGS：用于休闲视频的鲁棒动态高斯溅射