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ScribbleEdit dataset enhances AI image editing with text and scribbles

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ScribbleEdit, a new synthetic dataset designed to improve image editing by combining natural language instructions with freehand scribbles. This dataset aims to address the challenge of precisely controlling edits by providing a way for models to interpret both spatial layouts and semantic details simultaneously. By training on ScribbleEdit, image editing models, particularly diffusion-based and autoregressive ones, show significant improvements in generating spatially aligned and semantically consistent results. AI

IMPACT This dataset could enable more intuitive and precise AI-powered image editing tools.

RANK_REASON This is a research paper introducing a new dataset for image editing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ScribbleEdit dataset enhances AI image editing with text and scribbles

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This is a research paper introducing a new dataset for image editing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Anya Ji, George Ma, T\'ea Wright, Yiming Zhang, David Chan, Alane Suhr, Somayeh Sojoudi ·

    ScribbleEdit: Synthetic Data for Image Editing with Scribbles and Text

    arXiv:2605.01135v1 Announce Type: new Abstract: Recent progress in generative models has significantly advanced image editing capabilities, yet precise and intuitive user control remains difficult. Specifically, users often struggle to communicate both exact spatial layouts and s…

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