论文标题
DeepFacePencil：从徒手草图中创建面部图像
DeepFacePencil: Creating Face Images from Freehand Sketches
论文作者
论文摘要
在本文中，我们探讨了从手绘草图中生成照片真实的面部图像的任务。现有的图像到图像翻译方法需要大规模的配对草图和图像以进行监督。他们通常利用面部图像的合成边缘图作为训练数据。但是，这些合成的边缘图严格与相应面部图像的边缘一致，这限制了其概括能力，可以具有巨大的中风多样性的真实手绘草图。为了解决这个问题，我们提出了DeepFacePencil，这是一种有效的工具，能够根据训练期间的新型双发电机图像翻译网络从手绘草图中生成照片真实的面部图像。一种新型的空间注意力集合（SAP）旨在适应性地处理中风扭曲，该扭曲在空间上变化以支持各种冲程样式和不同级别的细节。我们进行了广泛的实验，结果证明了我们模型比现有方法在图像质量和模型概括方面的优越性，而不是手绘草图。
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.
