Both geometry and texture are fundamental aspects of visual style. Existing style transfer methods, however, primarily focus on texture, almost entirely ignoring geometry. We propose deformable style transfer (DST), an optimization-based approach that jointly stylizes the texture and geometry of a content image to better match a style image. Unlike previous geometry-aware stylization methods, our approach is neither restricted to a particular domain (such as human faces), nor does it require training sets of matching style/content pairs. We demonstrate our method on a diverse set of content and style images including portraits, animals, objects, scenes, and paintings. Code has been made publicly available at https://github.com/sunniesuhyoung/DST.

中文翻译：

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可变形风格转移

几何和纹理都是视觉风格的基本方面。然而，现有的风格转移方法主要关注纹理，几乎完全忽略几何。我们提出了可变形样式转移 (DST)，这是一种基于优化的方法，它联合样式化内容图像的纹理和几何形状以更好地匹配样式图像。与以前的几何感知风格化方法不同，我们的方法既不限于特定领域（例如人脸），也不需要匹配样式/内容对的训练集。我们在一系列不同的内容和风格图像上展示了我们的方法，包括肖像、动物、物体、场景和绘画。代码已在 https://github.com/sunniesuhyoung/DST 上公开提供。