Mitigating Content Shift and Hallucination in GenAI Image Editing via Structural Refinement
The paper proposes a post-processing framework that fuses an input image with its GenAI-enhanced counterpart to preserve perceptual enhancements while enforcing structural faithfulness, effectively mitigating spatial misalignment, texture distortion, and content hallucination. Experiments show it better preserves aesthetic quality and pixel-level structural consistency.
[2605.30437] Mitigating Content Shift and Hallucination in GenAI Image Editing via Structural Refinement
[Submitted on 28 May 2026]
Title:Mitigating Content Shift and Hallucination in GenAI Image Editing via Structural Refinement
View a PDF of the paper titled Mitigating Content Shift and Hallucination in GenAI Image Editing via Structural Refinement, by Luxi Zhao and 1 other authors
View PDF HTML (experimental)
Abstract:Generative AI (GenAI) image editors, such as Nano Banana, produce visually compelling results for retouching tasks, enabling non-experts to edit images through text prompts alone. However, the generative nature of these models often introduces spatial misalignment, texture distortion, and content hallucination, all of which are detrimental to downstream workflows that require pixel-level fidelity. We identify a problem setting we call "structure-preserving GenAI fusion" for black-box GenAI image retouching: retain the perceptual enhancements of a GenAI output while enforcing structural faithfulness to the original input image. To address this problem, we propose a post-processing framework that fuses an input image with its GenAI-enhanced counterpart by first establishing coarse spatial and photometric correspondences, then performing a fusion stage that transfers desired enhancements while suppressing hallucinated content. In the absence of direct prior work in this setting, we evaluate our framework against representative methods from photorealistic style transfer and image fusion. Our experiments demonstrate that our method better preserves aesthetic quality while maintaining pixel-level structural consistency and the input resolution.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.30437 [cs.CV]
(or arXiv:2605.30437v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.30437
arXiv-issued DOI via DataCite
Submission history
From: Luxi Zhao [view email] [v1] Thu, 28 May 2026 18:11:29 UTC (8,174 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Mitigating Content Shift and Hallucination in GenAI Image Editing via Structural Refinement, by Luxi Zhao and 1 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CV
new | recent | 2026-05
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)