1Xiamen University, 2The Hong Kong University of Science and Technology (Guangzhou), 3 The Chinese University of Hong Kong, 4Bytedance, 5National University of Singapore, 6Tsinghua University
📝 Overview
JarvisArt Teaser
JarvisArt workflow and results showcase
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
JarvisArt Demo
Local Retouching Case
JarvisArt Demo
JarvisArt supports multi-granularity retouching goals, ranging from scene-level adjustments to region-specific refinements. Users can perform intuitive, free-form edits through natural inputs such as text prompts and bounding boxes
📚 Citation
If you find JarvisArt useful in your research, please consider citing:
bibtex
1@article{jarvisart2025,
2title={JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent},
3 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},
4 year={2025},
5 journal={arXiv preprint arXiv:2506.17612}
6}
