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Updated: May 18, 2026
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Published on: August 30, 2013
Object-centric image editing via position-structure guided diffusion
Qi Si1, Xiangrui Chen1, Bo Wang2
1School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230601, China.
Summary
This study introduces a novel framework for precise object editing in complex images using latent diffusion models. The method enhances object localization and preserves background integrity for superior image manipulation results.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Image Processing
Background:
- Object-centric editing in complex scenes is challenging due to inaccurate localization and interference in diffusion models.
- Current methods struggle with precise object placement and maintaining background integrity during edits.
Purpose of the Study:
- To develop a training-free framework for fine-grained object-centric image editing.
- To address challenges in object localization and contextual integrity preservation.
Main Methods:
- A latent space optimization strategy refines cross-attention maps to disentangle object representations and improve spatial alignment.
- A region-aware fusion mechanism adaptively blends edited features with background information to prevent structural distortion.
Main Results:
- The proposed method demonstrates superior performance compared to state-of-the-art approaches on public benchmarks.
- Achieved significant improvements in both structural fidelity and semantic coherence of edited images.
Conclusions:
- The novel framework effectively tackles key challenges in object-centric image editing.
- Offers enhanced control and accuracy for complex image manipulation tasks.
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