WeatherEdit: Controllable Weather Editing with 4D Gaussian Field

Abstract: In this work, we present WeatherEdit, a novel weather editing pipeline for generating realistic weather effects with controllable types and severity in 3D scenes. Our approach is structured into two key components: weather background editing and weather particle construction. For weather background editing, we introduce an all-in-one adapter that integrates multiple weather styles into a single pretrained diffusion model, enabling the generation of diverse weather effects in 2D image backgrounds. During inference, we design a Temporal-View (TV-) attention mechanism that follows a specific order to aggregate temporal and spatial information, ensuring consistent editing across multi-frame and multi-view images. To construct the weather particles, we first reconstruct a 3D scene using the edited images and then introduce a dynamic 4D Gaussian field to generate snowflakes, raindrops and fog in the scene. The attributes and dynamics of these particles are precisely controlled through physical-based modelling and simulation, ensuring realistic weather representation and flexible severity adjustments. Finally, we integrate the 4D Gaussian field with the 3D scene to render consistent and highly realistic weather effects. Experiments on multiple driving datasets demonstrate that WeatherEdit can generate diverse weather effects with controllable condition severity, highlighting its potential for autonomous driving simulation in adverse weather. See project page: https://jumponthemoon.github.io/w-edit

WeatherEdit: Controllable Weather Editing with 4D Gaussian Field

The paper "WeatherEdit: Controllable Weather Editing with 4D Gaussian Field" proposes a novel methodology for synthesizing realistic weather effects in 3D scenes, with a focus on adaptability and dynamic control. This research addresses a longstanding challenge within computer vision, the generation of multi-weather conditions with precise control and realism. The proposed WeatherEdit framework offers a promising approach for applications such as autonomous driving simulation, augmented reality, and virtual scene synthesis.

The WeatherEdit framework is comprised of two primary components: weather background editing and weather particle modeling. The background editing utilizes an innovative all-in-one adapter, which incorporates multiple weather styles into a singular diffusion model. This facilitates the generation of diverse weather effects in 2D image backgrounds, overcoming the limitations of previous style transfer methods that are typically confined to a single weather condition. By integrating a Temporal-View attention mechanism, WeatherEdit ensures spatial and temporal consistency across multi-frame and multi-view images, thereby addressing the issue of inconsistent outputs in existing diffusion models.

The weather particle construction component involves the use of a dynamic 4D Gaussian field to synthesize weather particles such as snowflakes, raindrops, and fog. This approach allows for precise control over particle attributes and dynamics, utilizing physics-based modeling and simulation. The integration of these particles with a reconstructed 3D scene results in realistic, high-fidelity weather representation that can be adjusted for varying severities.

The experimental evaluation conducted on multiple driving datasets demonstrates the efficacy of WeatherEdit in generating diverse weather effects with controllable severity. The results indicate potential applications in the domain of autonomous driving, particularly under adverse weather conditions. The ability to simulate a range of weather scenarios with adjustable severity levels could enhance the robustness and reliability of autonomous driving systems.

Implications of this research extend to both practical and theoretical domains. Practically, WeatherEdit could facilitate the development of more accurate simulation environments for testing autonomous vehicles in controlled weather conditions, potentially accelerating the deployment of these technologies in real-world settings. Theoretically, the integration of a temporal-view attention mechanism and dynamic 4D Gaussian fields contributes to the advancement of image synthesis models, offering a roadmap for future research in controllable scene editing.

Looking forward, WeatherEdit's methodologies may pave the way for more sophisticated AI models capable of simulating complex environmental conditions with high fidelity. Future developments could explore enhanced adaptability to incorporate additional weather types, as well as the refinement of weather particle dynamics to account for micro-scale atmospheric phenomena. The integration of AI advancements in these areas could further enhance the realism and applicability of 3D weather scene synthesis systems.