arXiv cs.AI· Vincent Auriau, Emmanuel Malherbe, Matthieu Perrot·· 3 天前AI 评分20
弱分割引导 GAN 实现逼真图像颜色编辑
Weak Segmentation-Guided GAN for~Realistic Color Edition
AI 导读
研究者提出一种利用任意可微分分割模型的 GAN 架构，在多个用例上取得 SOTA 结果，生成图像既逼真又能精确控制颜色。该方法旨在同时解决传统分割加直方图匹配可控性强但缺乏真实感、GAN 真实感强却难以控制的问题。
正文 · 原文
Abstract:Editing the color of images in a realistic way finds many applications such as changing the perception of an image, data augmentation or film post processing. The design of an automatic tool is a complex and long addressed challenge. In particular, two properties are difficult to meet altogether: generating realistic results without artifacts and the possibility to precisely choose the future color. Conventional methods using segmentation and histogram matching maximize the controllability but also introduce a lack of realism or are complex to automate. On the contrary, GANs that specialize in realism are difficult to control. To overcome these challenges, we propose a novel GAN architecture leveraging any differentiable segmentation model. We demonstrate the genericness of our framework that presents state of the art results on different use cases. It generates images that look realistic while offering a precise color control.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.16620 [cs.AI] |
| (or arXiv:2610.16620v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.16620 arXiv-issued DOI via DataCite (pending registration) |
|
| Journal reference: | Image Analysis and Processing -- ICIAP 2023, 14233, Springer Nature Switzerland, pp.487-499, 2023, Lecture Notes in Computer Science |
Submission history
From: Vincent Auriau [view email] [via CCSD proxy]
[v1]
Mon, 12 Oct 2026 11:36:01 UTC (7,331 KB)
来源：arXiv cs.AI · arxiv.org