WaterGen: Decoupling Scene and Medium in Underwater Image Generation

Underwater computer vision tasks, such as detection, restora-tion, and segmentation, are limited by the scarcity of large-scale and di-verse training data. We introduce WaterGen, a method for generatinglarge-scale, realistic, and diverse underwater images that provides inde-pendent control of the scene and water medium conditions. Our approachtreats underwater image generation as the decoupled control of two fac-tors: realistic and diverse scene content (what is in the image), and accu-rate and controllable water medium effects (what the water does to theimage). Existing methods generally achieve only part of this objective:they either provide controllability with limited realism or diversity, orgenerate realistic scenes without accurately and independently modelingwater-medium effects. Our key insight, that allows us to avoid this com-promise, is that scene generation and medium modeling can be decoupledwithin a latent diffusion framework, enabling diverse scene generation to-gether 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 imagesso that it learns to generate diverse and realistic latent embeddings ofunderwater scene content without medium-induced degradation. Second,we formulate the physically accurate medium degradation synthesis asa conditional decoding process applied to these latent embeddings. Thisdecoupled design allows our model to generate diverse scenes with fullcontrol of underwater appearance. We leverage WaterGen to build large-scale synthetic underwater datasets that are diverse in scene structuresand accurate in water effects and pseudo-labels. We demonstrate that oursynthetic data consistently improve downstream performance in under-water restoration and semantic segmentation. Code and model weightsare available at https://github.com/jiayi-wu-umd/WaterGen.