PhotoAgent: 带探索性视觉审美规划的代理照片编辑
PhotoAgent: Exploratory Visual Aesthetic Planning with Large Vision Models
AI总结：
PhotoAgent通过探索性视觉审美规划实现自主照片编辑，无需用户逐步提示，提升图像质量和指令遵循度。
摘要：
随着生成模型的快速发展，基于指令的照片编辑在生成高质量图像方面展现出巨大潜力。然而，编辑质量高度依赖于精心设计的指令，将任务分解和排序的负担完全放在用户身上。为了实现自主照片编辑，我们提出了PhotoAgent，一个通过显式审美规划推进照片编辑的系统。具体而言，PhotoAgent将自主照片编辑视为一个长周期决策问题。它在用户审美意图上进行推理，通过树搜索规划多步骤编辑动作，并通过闭环执行与记忆和视觉反馈迭代优化结果，而无需逐步用户提示。为了支持现实世界场景中的可靠评估，我们引入了UGC-Edit，一个包含7000张照片和一个学习到的审美奖励模型的审美评估基准。我们还构建了一个包含1017张照片的测试集，以系统地评估自主照片编辑性能。大量实验表明，PhotoAgent在指令遵循和视觉质量方面均比基线方法有显著提升。项目页面是https://mdyao.github.io/PhotoAgent/。
英文摘要：
With the recent fast development of generative models, instruction-based image editing has shown great potential in generating high-quality images. However, the quality of editing highly depends on carefully designed instructions, placing the burden of task decomposition and sequencing entirely on the user. To achieve autonomous image editing, we present PhotoAgent, a system that advances image editing through explicit aesthetic planning. Specifically, PhotoAgent formulates autonomous image editing as a long-horizon decision-making problem. It reasons over user aesthetic intent, plans multi-step editing actions via tree search, and iteratively refines results through closed-loop execution with memory and visual feedback, without requiring step-by-step user prompts. To support reliable evaluation in real-world scenarios, we introduce UGC-Edit, an aesthetic evaluation benchmark consisting of 7,000 photos and a learned aesthetic reward model. We also construct a test set containing 1,017 photos to systematically assess autonomous photo editing performance. Extensive experiments demonstrate that PhotoAgent consistently improves both instruction adherence and visual quality compared with baseline methods. The project page is https://mdyao.github.io/PhotoAgent/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
