With recent advances in image-to-image translation tasks, remarkable progress has been witnessed in generating face images from sketches. However, existing methods frequently fail to generate images with details that are semantically and geometrically consistent with the input sketch, especially when various decoration strokes are drawn. To address this issue, we introduce a novel W-W+ encoder architecture to take advantage of the high expressive power of W+ space and semantic controllability of W space. We introduce an explicit intermediate representation for sketch semantic embedding. With a semantic feature matching loss for effective semantic supervision, our sketch embedding precisely conveys the semantics in the input sketches to the synthesized images. Moreover, a novel sketch semantic interpretation approach is designed to automatically extract semantics from vectorized sketches. We conduct extensive experiments on both synthesized sketches and hand-drawn sketches, and the results demonstrate the superiority of our method over existing approaches on both semantics-preserving and generalization ability.

翻译：随着图像到图像翻译任务的近期进展，从草图生成人脸图像方面取得了显著进步。然而，现有方法在生成与输入草图在语义和几何上保持一致细节的图像时常常失败，尤其是当绘制各种装饰线条时。为解决这一问题，我们提出了一种新颖的W-W+编码器架构，以利用W+空间的高表达能力和W空间的语义可控性。我们引入了一种显式的中间表示用于草图语义嵌入。通过语义特征匹配损失实现有效的语义监督，我们的草图嵌入能够将输入草图中的语义精确地传递到合成图像中。此外，我们设计了一种新颖的草图语义解释方法，以从矢量化的草图中自动提取语义。我们在合成草图和手绘草图上进行了大量实验，结果证明了我们的方法在语义保持和泛化能力方面优于现有方法。