Weather-Magician: Reconstruction and Rendering Framework for 4D Weather Synthesis In Real Time

2025-05-26

AI Summary

Existing methods struggle to simultaneously achieve high-fidelity 3D reconstruction of realistic weather effects and real-time 4D rendering, limiting applications in digital twins, VR/AR, and cinematic production. This paper introduces the first extension of Gaussian Splatting to the 4D spatiotemporal domain, proposing (i) an efficient neural radiance field–based scene reconstruction framework, (ii) a physics-guided spatiotemporal parametric weather modeling scheme, (iii) a lightweight real-time rendering pipeline, and (iv) a dynamic sampling strategy. Our approach supports flexible composition, smooth transitions, and fine-grained control of diverse weather phenomena—including rain, snow, and fog. It achieves ≥30 FPS real-time rendering on a single RTX 4090 GPU, substantially lowering hardware requirements. Quantitative and qualitative evaluations demonstrate state-of-the-art visual fidelity and temporal consistency, effectively breaking the long-standing trade-off between reconstruction accuracy and rendering efficiency in dynamic weather modeling.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

For tasks such as urban digital twins, VR/AR/game scene design, or creating synthetic films, the traditional industrial approach often involves manually modeling scenes and using various rendering engines to complete the rendering process. This approach typically requires high labor costs and hardware demands, and can result in poor quality when replicating complex real-world scenes. A more efficient approach is to use data from captured real-world scenes, then apply reconstruction and rendering algorithms to quickly recreate the authentic scene. However, current algorithms are unable to effectively reconstruct and render real-world weather effects. To address this, we propose a framework based on gaussian splatting, that can reconstruct real scenes and render them under synthesized 4D weather effects. Our work can simulate various common weather effects by applying Gaussians modeling and rendering techniques. It supports continuous dynamic weather changes and can easily control the details of the effects. Additionally, our work has low hardware requirements and achieves real-time rendering performance. The result demos can be accessed on our project homepage: weathermagician.github.io

Problem

• Reconstructing real scenes with 4D weather effects
• Overcoming limitations in current weather rendering algorithms
• Enabling real-time dynamic weather synthesis efficiently

Innovation

• Uses gaussian splatting for 4D weather synthesis
• Simulates dynamic weather with Gaussians modeling
• Achieves real-time rendering with low hardware