Controllable Weather Synthesis and Removal with Video Diffusion Models

Abstract: Generating realistic and controllable weather effects in videos is valuable for many applications. Physics-based weather simulation requires precise reconstructions that are hard to scale to in-the-wild videos, while current video editing often lacks realism and control. In this work, we introduce WeatherWeaver, a video diffusion model that synthesizes diverse weather effects -- including rain, snow, fog, and clouds -- directly into any input video without the need for 3D modeling. Our model provides precise control over weather effect intensity and supports blending various weather types, ensuring both realism and adaptability. To overcome the scarcity of paired training data, we propose a novel data strategy combining synthetic videos, generative image editing, and auto-labeled real-world videos. Extensive evaluations show that our method outperforms state-of-the-art methods in weather simulation and removal, providing high-quality, physically plausible, and scene-identity-preserving results over various real-world videos.

Controllable Weather Synthesis and Removal with Video Diffusion Models

The paper "Controllable Weather Synthesis and Removal with Video Diffusion Models" presents a novel framework, WeatherWeaver, for manipulating weather conditions in video content using video diffusion models. The approach leverages contemporary advancements in generative artificial intelligence to provide precise and photorealistic editing capabilities for weather simulation and removal, circumventing previous limitations associated with physics-based models and traditional video editing techniques.

Weather simulation in videos offers significant potential across several domains, from creative applications such as film production and augmented reality, to safety-critical fields like autonomous driving and robotics. However, existing physics-based weather simulation methods necessitate detailed scene reconstructions that are impractical for real-world settings, while traditional video editing methods lack the realism and control necessary for high-impact applications.

WeatherWeaver introduces two complementary video diffusion models tailored for this task: one for weather synthesis and one for weather removal. The weather synthesis model generates diverse weather effects such as rain, snow, fog, and clouds directly onto any given input video without requiring detailed 3D modeling. This model grants precise control over the type and intensity of weather effects, supporting complex interactions and seamless blending of various weather conditions. Contrarily, the weather removal model is responsible for excising existing weather phenomena in videos, preserving essential scene details in the process.

The framework adeptly overcomes the challenge of acquiring high-quality paired training data by proposing a novel data strategy. This involves a combination of synthetic videos, generative image editing, and auto-labeled real-world videos. The ability to generate data at scale greatly enhances the framework's realism and adaptability in practical applications.

WeatherWeaver's training process is pivotal. Initially, the weather removal model is trained on synthetic video datasets and generated image pairs, subsequently extending to auto-labeled real-world videos. This staged training strategy effectively synthesizes lessons from all types of data sources, ensuring both models are finely tuned and realistic in their output. Ultimately, extensive evaluations exhibit WeatherWeaver's superiority over state-of-the-art methods in both synthesis and removal abilities, boasting high-quality, physically plausible, and scene-preservation results across various real-world videos.

The implications of this work are multifaceted. Practically, it could revolutionize how weather conditions are edited in video content, providing filmmakers, game developers, and AR/VR creators with enhanced creative flexibility. From a theoretical standpoint, this framework invites further research into integrating diffusion models with environmental simulations, presenting opportunities to refine generative AI's capabilities in dynamic video editing tasks.

In conclusion, WeatherWeaver exemplifies a significant advancement in generative video editing, proposing an efficient and scalable solution to control weather effects in video sequences. Future research may explore extending this approach to include more diverse environmental changes, broadening its scope and adding to the robustness of AI-driven video editing solutions.