JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent
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Known fake/malicious repositories:
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📝 Overview
JarvisArt is a multi-modal large language model (MLLM)-driven agent for intelligent photo retouching. It is designed to liberate human creativity by understanding user intent, mimicking the reasoning of professional artists, and coordinating over 200 tools in Adobe Lightroom. JarvisArt utilizes a novel two-stage training framework, starting with Chain-of-Thought supervised fine-tuning for foundational reasoning, followed by Group Relative Policy Optimization for Retouching (GRPO-R) to enhance its decision-making and tool proficiency. Supported by the newly created MMArt dataset (55K samples) and MMArt-Bench, JarvisArt demonstrates superior performance, outperforming GPT-4o with a 60% improvement in pixel-level metrics for content fidelity while maintaining comparable instruction-following capabilities.
🎬 Demo Videos
Global Retouching Case
Local Retouching Case
📚 Citation
If you find JarvisArt useful in your research, please consider citing:
@article{jarvisart2025,
title={JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent},
author={Yunlong Lin and Zixu Lin and Kunjie Lin and Jinbin Bai and Panwang Pan and Chenxin Li and Haoyu Chen and Zhongdao Wang and Xinghao Ding and Wenbo Li and Shuicheng Yan},
year={2025},
journal={arXiv preprint arXiv:2506.17612}
}
📧 Contact
For any questions or inquiries, please reach out to us:
- Yunlong Lin: linyl@stu.xmu.edu.cn
- Zixu Lin: a860620266@gmail.com
- Kunjie Lin: linkunjie@stu.xmu.edu.cn
🙏 Acknowledgements
We would like to express our gratitude to LLaMA-Factory and gradio_image_annotator for their valuable open-source contributions which have provided important technical references for our work.