论文标题
可变形样式转移
Deformable Style Transfer
论文作者
论文摘要
几何和纹理都是视觉样式的基本方面。但是，现有的样式转移方法主要集中在纹理上，几乎完全忽略了几何形状。我们提出了可变形样式转移（DST），这是一种基于优化的方法，可以共同对内容图像的纹理和几何形状进行样式，以更好地匹配样式图像。与以前的几何学风格化方法不同，我们的方法既不仅限于特定领域（例如人脸），也不需要匹配样式/内容对的训练集。我们在各种内容和样式图像上演示了我们的方法，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。
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.
