Dynamic Scenes in Real-Time: 4D Gaussian Splatting Achieves New Speed Records

Rendering dynamic scenes in real-time is a central challenge in computer graphics. Methods must both accurately capture the complexity of the scene and be fast enough to enable interactive applications. 4D Gaussian Splatting (4DGS) has recently emerged as a promising approach that achieves high visual quality in the reconstruction of dynamic scenes through the use of Gaussian functions. However, previous implementations suffered from high memory requirements and slow rendering speeds, limiting their use in real-time applications.

New research now presents 4DGS-1K, an optimized variant of 4DGS that addresses these limitations and enables rendering speeds of over 1000 frames per second (FPS) on modern GPUs. The researchers identified two main causes for the previous inefficiency: short-lived Gaussian functions and inactive Gaussian functions during rendering.

Short-lived Gaussian functions are used in 4DGS to represent dynamic movements in the scene. However, these functions often exist only for short periods, leading to a large number of Gaussian functions and thus high memory requirements. To solve this problem, the researchers introduced the "Spatial-Temporal Variation Score." This new criterion allows short-lived Gaussian functions to be effectively removed while still capturing the dynamic aspects of the scene with longer-lived Gaussian functions.

The second optimization approach focuses on inactive Gaussian functions. During rendering, only a small subset of Gaussian functions contributes to each individual frame. However, previous implementations processed all Gaussian functions during rasterization, leading to unnecessary computational overhead. 4DGS-1K uses a mask to identify and store active Gaussian functions across consecutive frames. This significantly reduces the number of calculations during rasterization.

The results show that 4DGS-1K achieves a 41-fold reduction in memory requirements and a 9-fold increase in rasterization speed compared to conventional 4DGS in complex dynamic scenes, without significantly compromising visual quality. This performance improvement opens up new possibilities for the use of 4DGS in real-time applications such as virtual reality, augmented reality, and interactive simulations.

The development of 4DGS-1K represents a significant advance in the real-time rendering of dynamic scenes. By specifically optimizing memory management and the rendering pipeline, the researchers were able to significantly increase the performance of 4DGS and pave the way for new applications in various fields.

Bibliography: Wu et al. (2024). 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering. CVPR 2024. Yuan et al. (2025). 1000+ FPS 4D Gaussian Splatting for Dynamic Scene Rendering. arXiv preprint arXiv:2503.16422. Wu et al. (2024). 4D Gaussian Splatting: Modeling Dynamic Scenes with Native 4D Primitives. ECCV 2024. Wu et al. (2024). Scene-Consistent Gaussian Splatting. NeurIPS 2024. Hustvl/4DGaussians GitHub Repository. Guanjun Wu's Project Page on 4DGS.