WaterGen: Decoupling Scene and Medium in Underwater Image Generation
European Conference on Computer Vision (ECCV) — 2026
Underwater computer vision tasks, such as detection, restoration, and segmentation, are limited by the scarcity of large-scale and diverse training data. We introduce WaterGen, a method for generating large-scale, realistic, and diverse underwater images that provides independent control of the scene and water medium conditions. Our approach treats underwater image generation as the decoupled control of two factors: realistic and diverse scene content, and accurate and controllable water medium effects. Our key insight is that scene generation and medium modeling can be decoupled within a latent diffusion framework: we first fine-tune a latent diffusion U-Net on degradation-free underwater images to generate diverse, realistic scene content, then formulate physically accurate medium degradation as a conditional decoding process applied to these latents. We leverage WaterGen to build large-scale synthetic underwater datasets and demonstrate that our synthetic data consistently improve downstream performance in underwater restoration and semantic segmentation.
@article{wu2026watergen,
title={WaterGen: Decoupling Scene and Medium in Underwater Image Generation},
author={Wu, Jiayi and Wang, Tianfu and Xiong, Tianyi and Yuan, Dehao and Lin, Xiaomin and Islam, Md Jahidul and Fermuller, Cornelia and Metzler, Christopher and Aloimonos, Yiannis},
journal={arXiv preprint arXiv:2606.31147},
year={2026}
}