Retrospective novel view synthesis (NVS) of dynamic scenes is fundamental to applications such as sports. Recent dynamic 3D Gaussian Splatting (3DGS) approaches introduce temporally coupled formulations to enforce motion coherence across time. In this paper, we argue that, in a synchronized multi-view (MV) setting typical of sports, the dynamic scene at each time step is already strongly geometrically constrained. We posit that the availability of calibrated, synchronized viewpoints provides sufficient spatial consistency, and therefore, explicit temporal coupling, or complex multi-body constraints seems unnecessary for retrospective NVS. To this end, we propose an approach tailored for synchronized MV dynamic scene. By initializing the SfM-derived point cloud at the start time and propagating optimized Gaussians over time, we show that efficient retrospective NVS can be achieved without imposing a temporal deformation constraint. Complementing our methodological contribution, we introduce a Dynamic MV dataset framework built on Blender for reproducible NeRF and 3DGS research. The framework generates high-quality, synchronized camera rigs and exports training-ready datasets in standard formats, eliminating inconsistencies in coordinate conventions and data pipelines. Using the framework, we construct a dynamic benchmark suite and evaluate representative NeRF and 3DGS approaches under controlled conditions. Together, we show that, under a synchronized MV setup, efficient retrospective dynamic scene NVS can be achieved using 3DGS. At the same time, the dataset-generation framework enables reproducible and principled benchmarking of dynamic NVS methods.

翻译：回顾式新视角合成（NVS）在体育等动态场景应用中至关重要。近期动态3D高斯泼溅（3DGS）方法引入时间耦合公式来强制运动的时间连贯性。本文认为，在体育场景典型的同步多视角（MV）设定下，每个时间步的动态场景已受到强几何约束。我们提出：标定同步视角的可用性提供了充分的空间一致性，因此对于回顾式NVS而言，显式的时间耦合或复杂的多体约束似乎并无必要。为此，我们提出一种面向同步MV动态场景的专用方法。通过使用运动恢复结构（SfM）导出的点云在起始时刻初始化，并将优化后的高斯分布随时间传播，我们证明无需施加时间形变约束即可实现高效的回顾式NVS。作为方法贡献的补充，我们提出基于Blender构建的动态MV数据集框架，用于可复现的NeRF与3DGS研究。该框架可生成高质量同步相机阵列，并以标准格式导出可直接用于训练的数据集，消除坐标约定与数据管线的差异性。利用该框架，我们构建了动态基准测试套件，并在受控条件下评估代表性NeRF与3DGS方法。综合结果表明，在同步MV设定下，使用3DGS可实现高效的回顾式动态场景NVS；同时，所提出的数据集生成框架能够支持动态NVS方法的可复现性与规范性基准测试。