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Understanding Generative AI Capabilities in Everyday Image Editing Tasks (arxiv.org)
5 points by taesiri on May 23, 2025 | hide | past | pdf | 1 comment on HN

In plain words: By studying 83,000 real photo-editing requests posted to Reddit over 12 years, the work maps what people ask for and tests how well today's AI editors deliver. The best completed only about 33% of requests, and did worse on precise, predictable edits than open-ended ones.

Abstract

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

Mohammad Reza Taesiri, Brandon Collins, Logan Bolton, Viet Dac Lai, Franck Dernoncourt, Trung Bui, Anh Totti Nguyen
arXiv:2505.16181 · cs.CV, cs.AI · submitted May 22, 2025 · updated May 26, 2025
abstract · pdf · html · Code and qualitative examples are available at: https://psrdataset.github.io

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tldr; We find that GenAI can satisfy 1/3 of everyday image editing requests, while 2/3 of the requests are better handled by human image editors.