  In this work, we propose a novel system for smart copy-paste, enabling the
synthesis of high-quality results given a masked source image content and a
target image context as input. Our system naturally resolves both shading and
geometric inconsistencies between source and target image, resulting in a
merged result image that features the content from the pasted source image,
seamlessly pasted into the target context. Our framework is based on a novel
training image transformation procedure that allows to train a deep
convolutional neural network end-to-end to automatically learn a representation
that is suitable for copy-pasting. Our training procedure works with any image
dataset without additional information such as labels, and we demonstrate the
effectiveness of our system on two popular datasets, high-resolution face
images and the more complex Cityscapes dataset. Our technique outperforms the
current state of the art on face images, and we show promising results on the
Cityscapes dataset, demonstrating that our system generalizes to much higher
resolution than the training data.
