G-WADE: Geometry-Preserving Weather-Stylized Urban Assets Diffusion Enhancement

Extending 3D Gaussian Splatting (3DGS) urban assets under adverse weather (e.g., rain, snow) directly from real-world captures is prohibitively expensive, motivating the alternative of editing an existing, geometrically accurate 3DGS scene to synthesize new weather conditions. However, existing weather editing methods are limited in asset augmentation and prone to geometry collapse, multi-view texture inconsistency, and long-range drift. We observe that video diffusion models with depth/edge control can preserve scene geometry while re-stylizing appearance, but often produce substantial appearance discrepancies when revisiting previously seen regions, undermining multi-view consistency and degrading the quality of the resulting 3DGS reconstructions. In this paper, we propose G-WADE: Geometry-preserving Weather-stylized urban Assets Diffusion Enhancement, which is a geometry-fixed framework for the augmentation of weather-stylized urban assets. G-WADE fine-tunes two domain-specific variants of Cosmos-Transfer2.5 (Base-Model) by leveraging VAE temporal downsampling property: a first-frame-conditioned model (F-Model) and a first-last-frame-conditioned model (FL-Model) that extracts a tail latent slice as an additional anchor. Additionally, G-WADE designs trajectory-aware generation strategies with coordinated model scheduling, separately tailored for high-precision single-building capture (orbit-and-descend trajectories) and large-scale urban scenes (center-out grid trajectories). The generated multi-view-consistent, geometry-preserving frames are then used to optimize the attributes of the original 3DGS. Experiments on rain and snow weather stylization over both single-building and large-scale urban 3DGS scenes show that our method achieves effective urban asset augmentation, with better multi-view consistency, perceptual realism, and geometry preservation than existing video and 3D editing baselines.