Temporal Smoothness-Aware Rate-Distortion Optimized 4D Gaussian Splatting

2025-07-23

AI Summary

Dynamic 4D Gaussian Splatting (4DGS) suffers from prohibitive storage overhead due to its massive number of Gaussians, severe temporal redundancy, and lack of entropy-aware compression—hindering deployment on resource-constrained edge devices. To address this, we propose the first end-to-end rate-distortion optimized compression framework for 4DGS. Our method employs explicit dynamic Gaussian representations, models motion trajectories via wavelet transforms to enforce temporal smoothness priors, and introduces an entropy-aware coding module that jointly optimizes bitrate and rendering fidelity. The framework enables user-controllable accuracy–bitrate trade-offs. Implemented on Ex4DGS, our approach achieves up to 91× compression while preserving high visual fidelity, significantly improving cross-device real-time rendering efficiency.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Data Compression
• Computer Vision: Motion & Tracking

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Dynamic 4D Gaussian Splatting (4DGS) effectively extends the high-speed rendering capabilities of 3D Gaussian Splatting (3DGS) to represent volumetric videos. However, the large number of Gaussians, substantial temporal redundancies, and especially the absence of an entropy-aware compression framework result in large storage requirements. Consequently, this poses significant challenges for practical deployment, efficient edge-device processing, and data transmission. In this paper, we introduce a novel end-to-end RD-optimized compression framework tailored for 4DGS, aiming to enable flexible, high-fidelity rendering across varied computational platforms. Leveraging Fully Explicit Dynamic Gaussian Splatting (Ex4DGS), one of the state-of-the-art 4DGS methods, as our baseline, we start from the existing 3DGS compression methods for compatibility while effectively addressing additional challenges introduced by the temporal axis. In particular, instead of storing motion trajectories independently per point, we employ a wavelet transform to reflect the real-world smoothness prior, significantly enhancing storage efficiency. This approach yields significantly improved compression ratios and provides a user-controlled balance between compression efficiency and rendering quality. Extensive experiments demonstrate the effectiveness of our method, achieving up to 91x compression compared to the original Ex4DGS model while maintaining high visual fidelity. These results highlight the applicability of our framework for real-time dynamic scene rendering in diverse scenarios, from resource-constrained edge devices to high-performance environments.

Problem

• Reduces storage needs for 4D Gaussian Splatting videos
• Improves compression while maintaining rendering quality
• Enables real-time rendering on edge and high-performance devices

Innovation

• End-to-end RD-optimized compression for 4DGS
• Wavelet transform for temporal smoothness prior
• High compression ratio with visual fidelity