Guided Image Inpainting: Replacing an Image Region by Pulling Content From Another Image
Guided Image Inpainting: Replacing an Image Region by Pulling Content From Another Image
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DOI：
10.1109/wacv.2019.00166
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
Yinan Zhao;Brian L. Price;Scott D. Cohen;D. Gurari
中科院分区：
文献类型：
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作者：
Yinan Zhao;Brian L. Price;Scott D. Cohen;D. Gurari
深度生成模型在使用周围环境自动合成丢失的图像区域方面取得了成功。然而，用户不能直接决定什么样的内容合成与这样的approaches.We提出了一个端到端的网络图像修复，使用不同的图像来指导新内容的合成，以填补漏洞。我们的方法解决的一个关键挑战是在指导图像和原始图像的上下文不一致的区域合成新内容。我们进行了四项研究，证明我们的方法在七个基线上产生更逼真的图像修复结果。
Deep generative models have shown success in automatically synthesizing missing image regions using surrounding context. However, users cannot directly decide what content to synthesize with such approaches.We propose an end-to-end network for image inpainting that uses a different image to guide the synthesis of new content to fill the hole. A key challenge addressed by our approach is synthesizing new content in regions where the guidance image and the context of the original image are inconsistent. We conduct four studies that demonstrate our method yields more realistic image inpainting results over seven baselines.