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Cali-Sketch: Stroke Calibration and Completion for High-Quality Face Image Generation from Poorly-Drawn Sketches
Image generation task has received increasing attention because of its w...
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Deep Generation of Face Images from Sketches
Recent deep image-to-image translation techniques allow fast generation ...
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On the generalization of GAN image forensics
Recently the GAN generated face images are more and more realistic with ...
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Learning to Deblur Images with Exemplars
Human faces are one interesting object class with numerous applications....
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Stylizing Face Images via Multiple Exemplars
We address the problem of transferring the style of a headshot photo to ...
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Convolutional Sketch Inversion
In this paper, we use deep neural networks for inverting face sketches t...
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Face Destylization
Numerous style transfer methods which produce artistic styles of portrai...
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DeepFacePencil: Creating Face Images from Freehand Sketches
In this paper, we explore the task of generating photo-realistic face images from hand-drawn sketches. Existing image-to-image translation methods require a large-scale dataset of paired sketches and images for supervision. They typically utilize synthesized edge maps of face images as training data. However, these synthesized edge maps strictly align with the edges of the corresponding face images, which limit their generalization ability to real hand-drawn sketches with vast stroke diversity. To address this problem, we propose DeepFacePencil, an effective tool that is able to generate photo-realistic face images from hand-drawn sketches, based on a novel dual generator image translation network during training. A novel spatial attention pooling (SAP) is designed to adaptively handle stroke distortions which are spatially varying to support various stroke styles and different levels of details. We conduct extensive experiments and the results demonstrate the superiority of our model over existing methods on both image quality and model generalization to hand-drawn sketches.
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