Dotting the Eye: An Intent-Driven Image Retouching Agent for Visual Focus Enhancement

Image retouching is commonly formulated as enhancing over-all visual quality through color adjustment, but in practice, it also servesto emphasize visual focus by guiding viewers’ attention toward a specificsubject or region. Achieving such focus-oriented retouching is inherentlychallenging, as it requires well-coordinated global and local adjustmentsto manipulate perceptual saliency while maintaining visual naturalness.This intricate process typically demands substantial professional exper-tise. In this study, we propose EyeControl, a multi-modal large lan-guage model (MLLM)-driven agent with a diffusion-based retouchingexecutor that enables visual focus enhancement under weak user intent.With only a few clicks or coarse strokes, EyeControl directs visual at-tention to the intended region, effectively “dotting the eye” of the image.The core idea is to explicitly link the weak user intention with the tar-get editing region and the corresponding tonal adjustment operationsduring retouching. To achieve this, the system first interprets the in-tent and image content to infer the visual focus and generate structuredintent guidance for the retouching executor. Second, the retouching ex-ecutor is encouraged to respond more strongly to the target region, ex-plicitly aligning its attention map with a designed pseudo-intent map.We also introduce an operation-consistency constraint to improve coor-dination between global and local adjustments, achieving more naturaland coherent retouching. Additionally, we contribute ControlArt-Bench,a high-quality evaluation dataset for visual focus enhancement. Exten-sive evaluations demonstrate that EyeControl yields perceptually ap-pealing results with stronger intent alignment. Code will be released athttps://github.com/DragonisCV/EyeControl.