PhotoArtAgent: Intelligent Photo Retouching with Language Model-Based Artist Agents
Haoyu Chen, Keda Tao, Yizao Wang, Xinlei Wang, Lei Zhu, Jinjin Gu

TL;DR
PhotoArtAgent is an AI system that combines vision-language models and natural language reasoning to emulate professional artistic retouching, providing transparent, iterative, and user-controllable photo enhancement.
Contribution
It introduces a novel AI agent that plans, executes, and explains artistic photo retouching using vision-language models and API-driven adjustments.
Findings
Outperforms existing automated tools in user studies
Achieves results comparable to professional artists
Provides transparent, explainable retouching process
Abstract
Photo retouching is integral to photographic art, extending far beyond simple technical fixes to heighten emotional expression and narrative depth. While artists leverage expertise to create unique visual effects through deliberate adjustments, non-professional users often rely on automated tools that produce visually pleasing results but lack interpretative depth and interactive transparency. In this paper, we introduce PhotoArtAgent, an intelligent system that combines Vision-Language Models (VLMs) with advanced natural language reasoning to emulate the creative process of a professional artist. The agent performs explicit artistic analysis, plans retouching strategies, and outputs precise parameters to Lightroom through an API. It then evaluates the resulting images and iteratively refines them until the desired artistic vision is achieved. Throughout this process, PhotoArtAgent…
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