GaussianVideo: Efficient Video Representation via Hierarchical Gaussian Splatting (2501.04782v1)

Abstract: 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.

• The paper introduces a B-spline-based motion representation that ensures smooth camera trajectories and improved temporal consistency.
• It employs a hierarchical learning strategy to progressively refine spatial and temporal features, enhancing reconstruction quality and memory efficiency.
• It integrates Neural ODEs for adaptive camera motion modeling, achieving a PSNR improvement from 29.36 to 44.21 while lowering computational resource requirements.

Overview of GaussianVideo: Efficient Video Representation via Hierarchical Gaussian Splatting

This paper introduces a novel approach to video representation called GaussianVideo, which leverages hierarchical Gaussian splatting to model dynamic video scenes efficiently. The method is positioned as a solution to traditional video representation challenges such as high memory usage, lengthy training times, and lack of temporal consistency. By providing continuous scene representation and learning smooth camera trajectories, GaussianVideo is both a theoretical and practical advancement in video compression and interactive simulation applications.

Key Contributions

The core contributions of the research include:

1. B-Spline-Based Motion Representation: GaussianVideo introduces a B-spline-based motion representation that facilitates smooth and stable modeling of motion trajectories within scenes. This approach ensures temporal consistency while allowing for nuanced local variations and contrasts sharply with other methods that suffer from overfitting or instability due to polynomial motion modeling.
2. Hierarchical Learning Strategy: The paper proposes a hierarchical learning strategy to refine spatial and temporal features progressively. This strategy enables the model to capture finer details with enhanced reconstruction quality, improving both convergence speed and memory efficiency compared to existing techniques.
3. Camera Motion Modeling with Neural ODEs: The integration of Neural ODEs in GaussianVideo represents a significant departure from approaches that rely on precomputed camera parameters, such as those derived using COLMAP. This model improves adaptability to varying capture setups and further decreases dependency on external tools.

Experimental Analysis

The empirical results demonstrate that GaussianVideo achieves state-of-the-art performance on prevalent video datasets, including DL3DV and DAVIS. Notably, the approach yields a peak signal-to-noise ratio (PSNR) of 44.21, outperforming existing methods such as NeRV with a PSNR of 29.36 on comparable metrics, highlighting a 50.6% improvement. Moreover, GaussianVideo's ability to train with lower computational resources has been established, making it a compelling choice for memory-constrained environments.

Implications and Future Directions

From a practical standpoint, GaussianVideo has vast implications for any domain requiring efficient and high-quality video representation. The methodology is particularly robust for streaming platforms, special effects studios, and virtual reality applications, where high temporal consistency and reduced computational load are essential. Theoretically, the approach proposes an evolution in video representation paradigms, suggesting that traditional pixels may well be substituted by continuous neural representations without loss of fidelity and with substantial gains in flexibility.

The research opens several promising avenues for future exploration. Extensions of this work could investigate the incorporation of non-uniform rational B-splines (NURBS) to offer even greater motion complexity capture. Additionally, optimizing GaussianVideo for real-time applications or exploring its utility in unsupervised domains are exciting prospects.

In conclusion, GaussianVideo stands as a robust, efficient, and adaptive framework that pushes the boundaries of video representation by leveraging hierarchical Gaussian splatting to address longstanding challenges in the field, reflecting both theoretical innovation and practical utility.