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Position-Sensitive painterly image harmonization.

Han Guo1, Bolun Zheng1, Qianyu Zhang1

  • 1School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.

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|March 26, 2026
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Summary
This summary is machine-generated.

This study introduces a new method for seamlessly integrating photographic objects into paintings. It ensures visual harmony by considering both the overall art style and local details around the insertion point.

Keywords:
Image editingImage harmonization

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Area of Science:

  • Computer Vision
  • Digital Art
  • Image Processing

Background:

  • Painterly image harmonization aims to blend photographic objects into paintings.
  • Paintings have complex, spatially varying styles unlike realistic images.
  • Current methods often fail to harmonize objects with local painting styles.

Purpose of the Study:

  • To develop a method for position-sensitive painterly image harmonization.
  • To improve the visual consistency of inserted objects within diverse painting styles.
  • To address the limitations of existing methods in handling local style variations.

Main Methods:

  • Proposed a Position-aware Adaptive Instance Normalization (PAIN) module.
  • PAIN integrates global and local background styles based on insertion position.
  • Introduced a position-sensitive supervision method to detect and correct local inconsistencies.

Main Results:

  • The proposed method effectively harmonizes photographic objects with both global and local painting styles.
  • Achieved superior results in maintaining local style consistency compared to existing approaches.
  • Demonstrated effectiveness on public benchmark datasets.

Conclusions:

  • The position-sensitive approach is crucial for successful painterly image harmonization.
  • The PAIN module and position-sensitive supervision significantly enhance harmonization quality.
  • This method offers a robust solution for integrating objects into complex artistic images.