JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo
  Retouching Agent
Photo retouching has become integral to contemporary visual storytelling,
enabling users to capture aesthetics and express creativity. While professional
tools such as Adobe Lightroom offer powerful capabilities, they demand
substantial expertise and manual effort. In contrast, existing AI-based
solutions provide automation but often suffer from limited adjustability and
poor generalization, failing to meet diverse and personalized editing needs. To
bridge this gap, we introduce JarvisArt, a multi-modal large language model
(MLLM)-driven agent that understands user intent, mimics the reasoning process
of professional artists, and intelligently coordinates over 200 retouching
tools within Lightroom. JarvisArt undergoes a two-stage training process: an
initial Chain-of-Thought supervised fine-tuning to establish basic reasoning
and tool-use skills, followed by Group Relative Policy Optimization for
Retouching (GRPO-R) to further enhance its decision-making and tool
proficiency. We also propose the Agent-to-Lightroom Protocol to facilitate
seamless integration with Lightroom. To evaluate performance, we develop
MMArt-Bench, a novel benchmark constructed from real-world user edits.
JarvisArt demonstrates user-friendly interaction, superior generalization, and
fine-grained control over both global and local adjustments, paving a new
avenue for intelligent photo retouching. Notably, it outperforms GPT-4o with a
60% improvement in average pixel-level metrics on MMArt-Bench for content
fidelity, while maintaining comparable instruction-following capabilities.
Project Page: https://jarvisart.vercel.app/.
685b7538d2ee4fac76521e63
Yunlong Lin
claimed_verified
2025-06-25T08:07:25.557Z
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Yunlong Lin
user
LYL1015
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Zixu Lin
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Kunjie Lin
claimed_verified
2025-06-26T09:32:42.602Z
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linkunjie
user
linkunjie
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Jinbin Bai
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Panwang Pan
claimed_verified
2025-07-03T07:41:39.473Z
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Panwang Pan
user
paulpanwang
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Chenxin Li
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Haoyu Chen
685b7538d2ee4fac76521e6a
Zhongdao Wang
685b7538d2ee4fac76521e6b
Xinghao Ding
685b7538d2ee4fac76521e6c
Wenbo Li
685b7538d2ee4fac76521e6d
Shuicheng Yan
JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo
  Retouching Agent
Photo retouching has become integral to contemporary visual storytelling,
enabling users to capture aesthetics and express creativity. While professional
tools such as Adobe Lightroom offer powerful capabilities, they demand
substantial expertise and manual effort. In contrast, existing AI-based
solutions provide automation but often suffer from limited adjustability and
poor generalization, failing to meet diverse and personalized editing needs. To
bridge this gap, we introduce JarvisArt, a multi-modal large language model
(MLLM)-driven agent that understands user intent, mimics the reasoning process
of professional artists, and intelligently coordinates over 200 retouching
tools within Lightroom. JarvisArt undergoes a two-stage training process: an
initial Chain-of-Thought supervised fine-tuning to establish basic reasoning
and tool-use skills, followed by Group Relative Policy Optimization for
Retouching (GRPO-R) to further enhance its decision-making and tool
proficiency. We also propose the Agent-to-Lightroom Protocol to facilitate
seamless integration with Lightroom. To evaluate performance, we develop
MMArt-Bench, a novel benchmark constructed from real-world user edits.
JarvisArt demonstrates user-friendly interaction, superior generalization, and
fine-grained control over both global and local adjustments, paving a new
avenue for intelligent photo retouching. Notably, it outperforms GPT-4o with a
60% improvement in average pixel-level metrics on MMArt-Bench for content
fidelity, while maintaining comparable instruction-following capabilities.
Project Page: https://jarvisart.vercel.app/.
JarvisArt, an MLLM-driven agent, achieves superior photo retouching by understanding user intent and coordinating multiple retouching tools in Lightroom, outperforming GPT-4o on a novel benchmark.
multi-modal large language model Chain-of-Thought supervised fine-tuning Group Relative Policy Optimization Agent-to-Lightroom Protocol MMArt-Bench global adjustments local adjustments content fidelity instruction-following capabilities