RetouchLLM: Training-free Code-based Image Retouching with Vision Language Models

• URL: http://arxiv.org/abs/2510.08054v2
• Date: Fri, 10 Oct 2025 08:25:44 GMT
• Title: RetouchLLM: Training-free Code-based Image Retouching with Vision Language Models
• Authors: Moon Ye-Bin, Roy Miles, Tae-Hyun Oh, Ismail Elezi, Jiankang Deng,
• Abstract summary: We propose RetouchLLM, a training-free white-box image retouching system.<n>It performs interpretable, code-based retouching directly on high-resolution images.<n>Our framework progressively enhances the image in a manner similar to how humans perform multi-step retouching.
• Score: 76.79706360982162
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image retouching not only enhances visual quality but also serves as a means of expressing personal preferences and emotions. However, existing learning-based approaches require large-scale paired data and operate as black boxes, making the retouching process opaque and limiting their adaptability to handle diverse, user- or image-specific adjustments. In this work, we propose RetouchLLM, a training-free white-box image retouching system, which requires no training data and performs interpretable, code-based retouching directly on high-resolution images. Our framework progressively enhances the image in a manner similar to how humans perform multi-step retouching, allowing exploration of diverse adjustment paths. It comprises of two main modules: a visual critic that identifies differences between the input and reference images, and a code generator that produces executable codes. Experiments demonstrate that our approach generalizes well across diverse retouching styles, while natural language-based user interaction enables interpretable and controllable adjustments tailored to user intent.

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