Efficient neural representations for dynamic video scenes are critical for applications ranging from video compression to interactive simulations. Yet, existing methods often face challenges related to high memory usage, lengthy training times, and temporal consistency. To address these issues, we introduce a novel neural video representation that combines 3D Gaussian splatting with continuous camera motion modeling. By leveraging Neural ODEs, our approach learns smooth camera trajectories while maintaining an explicit 3D scene representation through Gaussians. Additionally, we introduce a spatiotemporal hierarchical learning strategy, progressively refining spatial and temporal features to enhance reconstruction quality and accelerate convergence. This memory-efficient approach achieves high-quality rendering at impressive speeds. Experimental results show that our hierarchical learning, combined with robust camera motion modeling, captures complex dynamic scenes with strong temporal consistency, achieving state-of-the-art performance across diverse video datasets in both high- and low-motion scenarios.

本研究解决了现有视频表示方法在内存使用、训练时间和时间一致性方面的局限。我们提出了一种新颖的神经视频表示方法，通过结合3D高斯绘制和连续摄像机运动建模，利用神经常微分方程（Neural ODEs）学习平滑的摄像机轨迹。实验结果表明，该方法在复杂动态场景中实现高质量渲染，并在高低运动场景的视频数据集上达到了最先进的性能。

高斯视频：通过层次高斯绘制实现高效视频表示