Gaussians on their Way: Wasserstein-Constrained 4D Gaussian Splatting with State-Space Modeling

2024-11-30

arXiv.org

AI Summary

To address unnatural and discontinuous temporal motion of Gaussian ellipsoids in dynamic 3D scenes, this paper proposes a 4D Gaussian splatting optimization framework integrating state-space modeling with Wasserstein-geometry constraints. Our method introduces three key innovations: (1) the State Consistency Filter—a novel spatiotemporal calibration mechanism that jointly leverages prior predictions and observations; (2) the first incorporation of Wasserstein distance regularization into 4D Gaussian representations, unifying translation and deformation modeling while ensuring physical plausibility and temporal coherence; and (3) a dynamic parameter smoothing update strategy. Evaluated on multiple dynamic datasets, our approach significantly reduces motion artifacts and achieves notable improvements in PSNR and SSIM over prior work, while maintaining higher inference efficiency than current state-of-the-art methods.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

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 scene rendering has taken a leap forward with the rise of 4D Gaussian Splatting, but there's still one elusive challenge: how to make 3D Gaussians move through time as naturally as they would in the real world, all while keeping the motion smooth and consistent. In this paper, we unveil a fresh approach that blends state-space modeling with Wasserstein geometry, paving the way for a more fluid and coherent representation of dynamic scenes. We introduce a State Consistency Filter that merges prior predictions with the current observations, enabling Gaussians to stay true to their way over time. We also employ Wasserstein distance regularization to ensure smooth, consistent updates of Gaussian parameters, reducing motion artifacts. Lastly, we leverage Wasserstein geometry to capture both translational motion and shape deformations, creating a more physically plausible model for dynamic scenes. Our approach guides Gaussians along their natural way in the Wasserstein space, achieving smoother, more realistic motion and stronger temporal coherence. Experimental results show significant improvements in rendering quality and efficiency, outperforming current state-of-the-art techniques.

Problem

• 3D Animation
• Smooth Transition
• Naturalistic Motion

Innovation

• Wasserstein Constraint
• State Space Modeling
• 4D Image Processing