Understanding Generative AI Capabilities in Everyday Image Editing Tasks
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Abstract (arXiv)
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
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 34 of its 58 references that are in the map. First posted on arXiv 22 May 2025.
Builds on
34 of 58 references.
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22 more
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Built on by
Citing papers per year: 2025: 1; 2026: 4.
5 papers.
- 2025PICABench: How Far Are We from Physically Realistic Image Editing?cited by 16
- 2026ImageEdit-R1: Boosting Multi-Agent Image Editing via Reinforcement Learningcited by 5
- 2026Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modelingcited by 5
- 2026Blind to the Human Touch: Overlap Bias in LLM-Based Summary Evaluationcited by 0
- 2026SketchVLM: Vision language models can annotate images to explain thoughts and guide userscited by 0
- 2026Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling30 Apr 2026
- 2026SketchVLM: Vision language models can annotate images to explain thoughts and guide users23 Apr 2026
- 2026ImageEdit-R1: Boosting Multi-Agent Image Editing via Reinforcement Learning9 Mar 2026
- 2026Blind to the Human Touch: Overlap Bias in LLM-Based Summary Evaluation7 Feb 2026
- 2025PICABench: How Far Are We from Physically Realistic Image Editing?20 Oct 2025
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