论文标题
基于示例的图像翻译的跨域对应学习
Cross-domain Correspondence Learning for Exemplar-based Image Translation
论文作者
论文摘要
我们提出了一个基于示例的图像翻译的一般框架，该框架从不同域中的输入（例如，语义分割掩码，边缘映射或姿势关键点）合成了光真实的图像，给定示例图像。输出具有与示例中语义对应对象一致的样式（例如，颜色，纹理）。我们建议共同学习跨域的对应关系和图像翻译，这两个任务都相互促进，因此可以通过弱监督来学习。首先将来自不同域的图像对齐到建立致密对应关系的中间域。然后，网络根据示例中语义相应的斑块的外观综合图像。我们在多个图像翻译任务中证明了方法的有效性。我们的方法在图像质量方面优于最先进的方法，具有语义一致性的图像样式忠于示例。此外，我们向多种应用显示了我们方法的实用性
We present a general framework for exemplar-based image translation, which synthesizes a photo-realistic image from the input in a distinct domain (e.g., semantic segmentation mask, or edge map, or pose keypoints), given an exemplar image. The output has the style (e.g., color, texture) in consistency with the semantically corresponding objects in the exemplar. We propose to jointly learn the crossdomain correspondence and the image translation, where both tasks facilitate each other and thus can be learned with weak supervision. The images from distinct domains are first aligned to an intermediate domain where dense correspondence is established. Then, the network synthesizes images based on the appearance of semantically corresponding patches in the exemplar. We demonstrate the effectiveness of our approach in several image translation tasks. Our method is superior to state-of-the-art methods in terms of image quality significantly, with the image style faithful to the exemplar with semantic consistency. Moreover, we show the utility of our method for several applications
