PerTouch: VLM-Driven Agent for Personalized and Semantic Image Retouching
This addresses the challenge of aligning image retouching with individual aesthetic preferences, though it appears incremental as it builds on existing diffusion and VLM methods.
The paper tackles the problem of personalized image retouching by balancing controllability and subjectivity, proposing PerTouch, a diffusion-based framework that uses parameter maps and a VLM-driven agent to achieve fine-grained enhancements, with experiments showing superior performance.
Image retouching aims to enhance visual quality while aligning with users' personalized aesthetic preferences. To address the challenge of balancing controllability and subjectivity, we propose a unified diffusion-based image retouching framework called PerTouch. Our method supports semantic-level image retouching while maintaining global aesthetics. Using parameter maps containing attribute values in specific semantic regions as input, PerTouch constructs an explicit parameter-to-image mapping for fine-grained image retouching. To improve semantic boundary perception, we introduce semantic replacement and parameter perturbation mechanisms in the training process. To connect natural language instructions with visual control, we develop a VLM-driven agent that can handle both strong and weak user instructions. Equipped with mechanisms of feedback-driven rethinking and scene-aware memory, PerTouch better aligns with user intent and captures long-term preferences. Extensive experiments demonstrate each component's effectiveness and the superior performance of PerTouch in personalized image retouching. Code is available at: https://github.com/Auroral703/PerTouch.