WaterGen: Decoupling Scene and Medium in Underwater Image Generation

2026-06-30

AI Summary

This work addresses the scarcity of real-world training data in underwater computer vision by proposing a two-stage latent diffusion generation framework that fully disentangles scene content from water medium effects in the latent space for the first time. The method first synthesizes degradation-free latent representations of underwater scenes and then applies a physically accurate underwater optical degradation model. Through fine-tuning of the U-Net architecture and a conditional decoding mechanism, the approach enables independent control over image content and underwater degradations. This allows the generation of a large-scale synthetic underwater dataset that preserves both diversity and photorealism, significantly improving performance on downstream tasks such as image restoration and semantic segmentation.

Abstract

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 (what is in the image), and accurate and controllable water medium effects (what the water does to the image). Existing methods generally achieve only part of this objective: they either provide controllability with limited realism or diversity, or generate realistic scenes without accurately and independently modeling water-medium effects. Our key insight, that allows us to avoid this compromise, is that scene generation and medium modeling can be decoupled within a latent diffusion framework, enabling diverse scene generation together with accurate and controllable underwater appearance. To do this, we decompose underwater image synthesis into two stages. First, we fine-tune the latent diffusion U-Net using degradation-free underwater images so that it learns to generate diverse and realistic latent embeddings of underwater scene content without medium-induced degradation. Second, we formulate the physically accurate medium degradation synthesis as a conditional decoding process applied to these latent embeddings. This decoupled design allows our model to generate diverse scenes with full control of underwater appearance. We leverage WaterGen to build large-scale synthetic underwater datasets that are diverse in scene structures and accurate in water effects and pseudo-labels. We demonstrate that our synthetic data consistently improve downstream performance in underwater restoration and semantic segmentation.

Problem

• underwater image generation
• scene content
• water medium effects
• training data scarcity
• realism and diversity

Innovation

• decoupled generation
• underwater image synthesis
• latent diffusion model
• medium degradation modeling
• synthetic dataset