理解生成式AI在日常图像编辑任务中的能力
Understanding Generative AI Capabilities in Everyday Image Editing Tasks
AI总结：
本文通过分析Reddit上83k个真实图像编辑请求，评估了生成式AI（如GPT-4o）的能力，发现仅约33%的请求可被最佳AI编辑器完成，且AI在低创造性精确编辑任务上表现更差，常难以保持身份特征。
摘要：
生成式人工智能（GenAI）在自动化日常图像编辑任务方面展现出巨大潜力，尤其是在2025年3月25日GPT-4o发布之后。然而，人们最常希望编辑的对象是什么？他们希望执行哪些类型的编辑操作（例如，移除或风格化对象）？人们更倾向于可预测结果的精确编辑还是高度创造性的编辑？通过理解真实世界请求的特征以及自由职业照片编辑专家所做的相应编辑，我们能否为改进基于AI的编辑器汲取经验教训，并确定哪些类型的请求目前可以由AI编辑器成功处理？在本文中，我们通过分析Reddit社区过去12年（2013-2025年）的83k个请求（这些请求收集了305k个PSR专家编辑）进行了一项独特的研究来回答这些问题。根据人类评分，大约只有33%的请求可以由最佳AI编辑器（包括GPT-4o、Gemini-2.0-Flash、SeedEdit）完成。有趣的是，AI编辑器在需要精确编辑的低创造性请求上的表现，比在更开放的任务上更差。它们常常难以保持人物和动物的身份特征，并且经常进行未经请求的修饰。另一方面，VLM评判者（例如o1）的表现与人类评判者不同，可能更偏好AI编辑而非人类编辑。代码和定性示例可在以下网址获取：https://psrdataset.github.io
英文摘要：
Generative AI (GenAI) holds significant promise for automating everyday image editing tasks, especially following the recent release of GPT-4o on March 25, 2025. However, what subjects do people most often want edited? What kinds of editing actions do they want to perform (e.g., removing or stylizing the subject)? Do people prefer precise edits with predictable outcomes or highly creative ones? By understanding the characteristics of real-world requests and the corresponding edits made by freelance photo-editing wizards, can we draw lessons for improving AI-based editors and determine which types of requests can currently be handled successfully by AI editors? In this paper, we present a unique study addressing these questions by analyzing 83k requests from the past 12 years (2013-2025) on the Reddit community, which collected 305k PSR-wizard edits. According to human ratings, approximately only 33% of requests can be fulfilled by the best AI editors (including GPT-4o, Gemini-2.0-Flash, SeedEdit). Interestingly, AI editors perform worse on low-creativity requests that require precise editing than on more open-ended tasks. They often struggle to preserve the identity of people and animals, and frequently make non-requested touch-ups. On the other side of the table, VLM judges (e.g., o1) perform differently from human judges and may prefer AI edits more than human edits. Code and qualitative examples are available at: https://psrdataset.github.io
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。
