Facial image inpainting is a task of filling visually realistic and semantically meaningful contents for missing or masked pixels in a face image. This paper presents EXE-GAN, a novel diverse and interactive facial inpainting framework, which can not only preserve the high-quality visual effect of the whole image but also complete the face image with exemplar-like facial attributes.
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