RetouchIQ: 基于指令的图像修复MLLM代理与通用奖励模型
RetouchIQ: MLLM Agents for Instruction-Based Image Retouching with Generalist Reward
AI总结：
RetouchIQ通过通用奖励模型提升指令基于图像编辑的语义一致性和感知质量，展现灵活可解释的编辑能力。
摘要：
最近多模态大语言模型（MLLMs）的进展展示了将视觉语言推理扩展到基于专业工具的图像编辑中的巨大潜力，使直观和创意编辑成为可能。一个有前途的方向是利用强化学习（RL）使MLLMs能够在专业图像编辑软件中推理并执行最佳工具使用计划。然而，训练仍然具有挑战性，因为缺乏可靠、可验证的奖励信号，这些信号能够反映创造性编辑本质上主观的性质。在本文中，我们介绍了RetouchIQ，一个通过MLLM代理引导的基于指令的可执行图像编辑框架。RetouchIQ解读用户指定的编辑意图并生成相应的可执行图像调整，将高级审美目标与精确参数控制联系起来。为了超越传统基于规则的奖励，这些奖励通过手工制作的指标计算与固定参考图像的相似性，我们提出了一种通用奖励模型，一个经过强化学习微调的MLLM，它通过一组生成的指标在个案基础上评估编辑结果。然后，奖励模型通过多模态推理提供标量反馈，使强化学习具有高质量、符合指令的梯度。我们整理了一个包含190,000个指令推理对的扩展数据集，并建立了一个新的基于指令的图像编辑基准。实验表明，RetouchIQ在语义一致性和感知质量方面显著优于之前的基于MLLM和扩散的编辑系统。我们的发现展示了通用奖励驱动的MLLM代理作为灵活、可解释和可执行的专业图像编辑助手的潜力。
英文摘要：
Recent advances in multimodal large language models (MLLMs) have shown great potential for extending vision-language reasoning to professional tool-based image editing, enabling intuitive and creative editing. A promising direction is to use reinforcement learning (RL) to enable MLLMs to reason about and execute optimal tool-use plans within professional image-editing software. However, training remains challenging due to the lack of reliable, verifiable reward signals that can reflect the inherently subjective nature of creative editing. In this work, we introduce RetouchIQ, a framework that performs instruction-based executable image editing through MLLM agents guided by a generalist reward model. RetouchIQ interprets user-specified editing intentions and generates corresponding, executable image adjustments, bridging high-level aesthetic goals with precise parameter control. To move beyond conventional, rule-based rewards that compute similarity against a fixed reference image using handcrafted metrics, we propose a generalist reward model, an RL fine-tuned MLLM that evaluates edited results through a set of generated metrics on a case-by-case basis. Then, the reward model provides scalar feedback through multimodal reasoning, enabling reinforcement learning with high-quality, instruction-consistent gradients. We curate an extended dataset with 190k instruction-reasoning pairs and establish a new benchmark for instruction-based image editing. Experiments show that RetouchIQ substantially improves both semantic consistency and perceptual quality over previous MLLM-based and diffusion-based editing systems. Our findings demonstrate the potential of generalist reward-driven MLLM agents as flexible, explainable, and executable assistants for professional image editing.
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