3D Gaussian Splatting for Efficient Retrospective Dynamic Scene Novel View Synthesis with a Standardized Benchmark

Problem
This paper addresses the gap in retrospective novel view synthesis (NVS) for dynamic scenes, particularly in synchronized multi-view settings typical of sports applications. Existing methods often rely on complex temporal coupling and multi-body constraints, which may be unnecessary given the strong geometric constraints provided by calibrated, synchronized viewpoints. The authors present a preprint work that proposes a more efficient approach to NVS without these constraints.

Method
The authors introduce a novel approach that leverages 3D Gaussian Splatting (3DGS) tailored for synchronized multi-view dynamic scenes. Their method begins with a Structure from Motion (SfM)-derived point cloud initialized at the first time step. Instead of imposing temporal deformation constraints, the method propagates optimized Gaussian representations over time, allowing for efficient retrospective NVS. The authors also present a Dynamic Multi-View (MV) dataset framework built on Blender, which generates high-quality synchronized camera rigs and exports datasets in standardized formats. This framework aims to eliminate inconsistencies in coordinate conventions and data pipelines, facilitating reproducibility in NeRF and 3DGS research.

Results
The authors evaluate their method against established baselines in the context of their newly constructed dynamic benchmark suite. While specific numerical results are not detailed in the abstract, the paper claims that their approach achieves efficient retrospective dynamic scene NVS under synchronized MV setups, outperforming existing NeRF and 3DGS methods. The introduction of the dataset framework is expected to enhance the reproducibility and comparability of results across different dynamic NVS methods.

Limitations
The authors acknowledge that their approach may not generalize well to unsynchronized or less constrained environments, where temporal coupling could be beneficial. Additionally, the reliance on a Blender-based dataset generation framework may limit the applicability of their findings to real-world scenarios where such controlled conditions are not present. The paper does not discuss potential computational overheads associated with the Gaussian propagation process or the scalability of their method to larger datasets.

Why it matters
This work has significant implications for the field of dynamic scene synthesis, particularly in applications requiring real-time processing, such as sports broadcasting and virtual reality. By demonstrating that efficient retrospective NVS can be achieved without complex temporal constraints, the authors pave the way for simpler and faster algorithms that can be deployed in practical scenarios. Furthermore, the introduction of a standardized dataset framework enhances the reproducibility of research in this area, encouraging further exploration and development of dynamic NVS techniques.

Authors: Yunxiao Zhang, Suryansh Kumar
Source: arXiv:2605.12437
URL: https://arxiv.org/abs/2605.12437v1