With the development of generative adversarial networks (GAN), the field of face sketch synthesis has received extensive attention. Face sketch synthesis (FSS) has promising prospects in the fields of entertainment and law enforcement, where it plays an increasingly important role. We propose a novel generative adversarial network for synthesizing sketches with similar shapes and rich details to photos. This problem is challenging because it involves the transition between the sketch domain and the photo domain. Many methods have been used for face sketch synthesis in recent years, but existing methods cannot fully exploit the semantic information between different domains. To this end, we use a cross-domain face sketch synthesis framework based on edge-preserving filters to make the boundaries of different semantics in semantic layouts have a smooth transition. We further propose a new spatially adaptive denormalization module named edge-aware enhancement Spatially Adaptive DEnormalization (eaeSPADE), which can make full use of the semantic information in the semantic layout of faces and improve the details of the synthesized face images. Extensive experiments demonstrate that our method outperforms existing face sketch synthesis methods.

中文翻译：

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用于面部草图合成的边缘感知域转换

随着生成对抗网络（GAN）的发展，人脸素描合成领域受到了广泛的关注。面部素描合成（FSS）在娱乐和执法领域具有广阔的前景，在其中发挥着越来越重要的作用。我们提出了一种新颖的生成对抗网络，用于将具有相似形状和丰富细节的草图与照片合成。这个问题具有挑战性，因为它涉及草图域和照片域之间的转换。近年来，人脸草图合成的方法很多，但现有方法无法充分利用不同域之间的语义信息。为此，我们使用基于边缘保留过滤器的跨域人脸草图合成框架，使语义布局中不同语义的边界具有平滑过渡。我们进一步提出了一种新的空间自适应去规范化模块，称为边缘感知增强空间自适应去规范化（eaeSPADE），它可以充分利用人脸语义布局中的语义信息，改善合成人脸图像的细节。大量实验表明，我们的方法优于现有的面部草图合成方法。可以充分利用人脸语义布局中的语义信息，提高合成人脸图像的细节。大量实验表明，我们的方法优于现有的面部草图合成方法。可以充分利用人脸语义布局中的语义信息，提高合成人脸图像的细节。大量实验表明，我们的方法优于现有的面部草图合成方法。