WeatherCity: Urban Scene Reconstruction with Controllable Multi-Weather Transformation

• URL: http://arxiv.org/abs/2602.22096v1
• Date: Wed, 25 Feb 2026 16:44:12 GMT
• Title: WeatherCity: Urban Scene Reconstruction with Controllable Multi-Weather Transformation
• Abstract summary: We propose WeatherCity, a novel framework for 4D urban scene reconstruction and weather editing.<n> Specifically, we leverage a text-guided image editing model to achieve flexible editing of image weather backgrounds.<n>Our framework achieves flexible controllability, high fidelity, and temporal consistency in 4D reconstruction and weather editing.
• Score: 19.48387412274678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Editable high-fidelity 4D scenes are crucial for autonomous driving, as they can be applied to end-to-end training and closed-loop simulation. However, existing reconstruction methods are primarily limited to replicating observed scenes and lack the capability for diverse weather simulation. While image-level weather editing methods tend to introduce scene artifacts and offer poor controllability over the weather effects. To address these limitations, we propose WeatherCity, a novel framework for 4D urban scene reconstruction and weather editing. Specifically, we leverage a text-guided image editing model to achieve flexible editing of image weather backgrounds. To tackle the challenge of multi-weather modeling, we introduce a novel weather Gaussian representation based on shared scene features and dedicated weather-specific decoders. This representation is further enhanced with a content consistency optimization, ensuring coherent modeling across different weather conditions. Additionally, we design a physics-driven model that simulates dynamic weather effects through particles and motion patterns. Extensive experiments on multiple datasets and various scenes demonstrate that WeatherCity achieves flexible controllability, high fidelity, and temporal consistency in 4D reconstruction and weather editing. Our framework not only enables fine-grained control over weather conditions (e.g., light rain and heavy snow) but also supports object-level manipulation within the scene.

Related papers

• Semantic-Aware, Physics-Informed, Geometry-Grounded Weather Video Synthesis [81.61856788730198]
  Weather synthesis aims to add weather effects to input videos while preserving scene identity, structure, and motion.<n>Most approaches rely on text prompts, which are inherently underspecified and often fail to produce detailed weather characteristics.<n>We propose a Semantic-Aware, Physics-Informed, and Geometry-Grounded framework that steers an off-the-shelf video editor to synthesize diverse global appearances and detailed particle dynamics.
  arXiv  Detail & Related papers  (2026-06-27T17:38:21Z)
• Unifying Physically-Informed Weather Priors in A Single Model for Image Restoration Across Multiple Adverse Weather Conditions [61.27301865260939]
  Weather-related artifacts vary with the particle's distance to the camera according to the established scene visibility analysis.<n>Existing methods fail to consider this weather-specific physical visual process.<n>We present a unified imaging model that considers the individually visible particles and fog-like aggregate scattering effects.
  arXiv  Detail & Related papers  (2026-05-13T08:22:23Z)
• SceneCrafter: Controllable Multi-View Driving Scene Editing [44.91248700043744]
  We propose SceneCrafter, a versatile editor for realistic 3D-consistent manipulation of driving scenes captured from multiple cameras.<n>SceneCrafter achieves state-of-the-art realism, controllability, 3D consistency, and scene editing quality compared to existing baselines.
  arXiv  Detail & Related papers  (2025-06-24T10:23:47Z)
• WeatherEdit: Controllable Weather Editing with 4D Gaussian Field [5.240297013713328]
  We present WeatherEdit, a novel weather editing pipeline for generating realistic weather effects in 3D scenes.<n>Our approach is structured into two key components: weather background editing and weather particle construction.<n>Experiments on multiple driving datasets demonstrate that WeatherEdit can generate diverse weather effects with controllable condition severity.
  arXiv  Detail & Related papers  (2025-05-26T19:10:47Z)
• Weather-Magician: Reconstruction and Rendering Framework for 4D Weather Synthesis In Real Time [28.860317925222954]
  We propose a framework based on gaussian splatting to reconstruct real scenes and render them under synthesized 4D weather effects.<n>Our work supports continuous dynamic weather changes and can easily control the details of the effects.
  arXiv  Detail & Related papers  (2025-05-26T12:44:53Z)
• Controllable Weather Synthesis and Removal with Video Diffusion Models [61.56193902622901]
  WeatherWeaver is a video diffusion model that synthesizes diverse weather effects directly into any input video.<n>Our model provides precise control over weather effect intensity and supports blending various weather types, ensuring both realism and adaptability.
  arXiv  Detail & Related papers  (2025-05-01T17:59:57Z)
• StreetCrafter: Street View Synthesis with Controllable Video Diffusion Models [76.62929629864034]
  We introduce StreetCrafter, a controllable video diffusion model that utilizes LiDAR point cloud renderings as pixel-level conditions.<n>In addition, the utilization of pixel-level LiDAR conditions allows us to make accurate pixel-level edits to target scenes.<n>Our model enables flexible control over viewpoint changes, enlarging the view for satisfying rendering regions.
  arXiv  Detail & Related papers  (2024-12-17T18:58:55Z)
• WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning [69.82211470647349]
  We introduce the first generalist weather foundation model (WeatherGFM)<n>It addresses a wide spectrum of weather understanding tasks in a unified manner.<n>Our model can effectively handle up to ten weather understanding tasks, including weather forecasting, super-resolution, weather image translation, and post-processing.
  arXiv  Detail & Related papers  (2024-11-08T09:14:19Z)
• Multiple weather images restoration using the task transformer and adaptive mixup strategy [14.986500375481546]
  We introduce a novel multi-task severe weather removal model that can effectively handle complex weather conditions in an adaptive manner. Our model incorporates a weather task sequence generator, enabling the self-attention mechanism to selectively focus on features specific to different weather types. Our proposed model has achieved state-of-the-art performance on the publicly available dataset.
  arXiv  Detail & Related papers  (2024-09-05T04:55:40Z)
• ScatterNeRF: Seeing Through Fog with Physically-Based Inverse Neural Rendering [83.75284107397003]
  We introduce ScatterNeRF, a neural rendering method which renders scenes and decomposes the fog-free background. We propose a disentangled representation for the scattering volume and the scene objects, and learn the scene reconstruction with physics-inspired losses. We validate our method by capturing multi-view In-the-Wild data and controlled captures in a large-scale fog chamber.
  arXiv  Detail & Related papers  (2023-05-03T13:24:06Z)
• ClimateNeRF: Extreme Weather Synthesis in Neural Radiance Field [57.859851662796316]
  We describe a novel NeRF-editing procedure that can fuse physical simulations with NeRF models of scenes. Results are significantly more realistic than those from SOTA 2D image editing and SOTA 3D NeRF stylization.
  arXiv  Detail & Related papers  (2022-11-23T18:59:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.