arXiv:2606.29020v1 Announce Type: cross Abstract: Weather synthesis aims to add weather effects to input videos while preserving scene identity, structure, and motion. The key limitation of existing methods is the lack of diversity in weather appearance and effective control over weather dynamics (e.g., temporal evolution and particle motion). Most approaches rely on text prompts, which are inherently underspecified and often fail to produce detailed weather characteristics. Additionally, general-purpose video editors optimized for clean and aesthetic outputs tend to suppress heavy weather phe
Advancements in AI, particularly in generative models, are enabling more sophisticated and controllable video synthesis techniques, moving beyond simple text prompts.
Improved weather synthesis offers significant commercial potential for industries requiring realistic environmental simulation and detailed visual effects, reducing costs and increasing fidelity.
The ability to generate diverse and accurately dynamic weather effects in videos will improve simulation realism for training, entertainment, and potentially forecasting models.
- · Entertainment industry
- · Simulation training developers
- · AI model developers
- · Climate modeling researchers
- · Traditional visual effects studios (if they don't adapt)
More realistic and varied weather effects become readily accessible for video content creation and simulation.
This synthesis capability could enhance climate change impact visualizations and disaster preparedness simulations.
The technology might enable new forms of AI-driven scenario planning for urban development and infrastructure design through highly accurate environmental simulations.
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Read at arXiv cs.AI