mlsanityData to 14 Oct 2026

Understanding Generative AI Capabilities in Everyday Image Editing Tasks

arXiv 2505.16181 ↗First posted 22 May 2025cs.CV cs.AIcited by 5

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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.

References · 34

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22 more
  1. 2023Emu Edit: Precise Image Editing via Recognition and Generation Taskscited by 324
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  13. 2024I2EBench: A Comprehensive Benchmark for Instruction-based Image Editingcited by 63
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Generative AI for Film Creation: A Survey of Recent Advances
  2. 2026EPEdit: Redefining Image Editing with Generative AI and User-Centric Design
  3. 2026PhotoAgent: Exploratory Visual Aesthetic Planning with Large Vision Models
  4. 2025Generative AI for Software Architecture. Applications, Challenges, and Future Directions
  5. 2026VIBE: Visual Instruction Based Editor
  6. 2024Facilitating Human Feedback for GenAI Prompt Optimization
  7. 2023ChatGPT is not all you need. A State of the Art Review of large Generative AI models
  8. 2024No Longer Trending on Artstation: Prompt Analysis of Generative AI Art