Recent color transfer methods use local information to learn the transformation from a source to an exemplar image, and then transfer this appearance change to a target image. These solutions achieve very successful results for general mood changes, e.g., changing the appearance of an image from “sunny” to “overcast”. However, such methods have a hard time creating new image content, such as leaves on a bare tree. Texture transfer, on the other hand, can synthesize such content but tends to destroy image structure. We propose the first algorithm that unifies color and texture transfer, outperforming both by leveraging their respective strengths. A key novelty in our approach resides in teasing apart appearance changes that can be modeled simply as changes in color versus those that require new image content to be generated. Our method starts with an analysis phase which evaluates the success of color transfer by comparing the exemplar with the source. This analysis then drives a selective, iterative texture transfer algorithm that simultaneously predicts the success of color transfer on the target and synthesizes new content where needed. We demonstrate our unified algorithm by transferring large temporal changes between photographs, such as change of season – e.g., leaves on bare trees or piles of snow on a street – and flooding.翻译为中文

最近的颜色转换方法利用局部信息来学习从源图像到示例图像的转换，并将这种外观变化传递到目标图像。这些解决方案在一般情况下取得了非常成功的结果，例如将图像的外观从“晴朗”变为“阴天”。然而，这样的方法很难创建新的图像内容，例如裸树上的树叶。另一方面，纹理转换可以合成这样的内容，但往往会破坏图像结构。我们提出了第一个统一颜色和纹理转换的算法，通过利用它们各自的优势，优于两者。我们方法的一个关键创新在于将可以简单地建模为颜色变化的外观变化与需要生成新图像内容的变化区分开来。我们的方法始于一个分析阶段，通过比较示例和源来评估颜色转换的成功。然后，这个分析驱动一个有选择性的、迭代的纹理转换算法，同时预测颜色转换在目标上的成功，并在需要时合成新的内容。我们通过将大的时间变化，例如季节的变化（例如树上的叶子或街道上的积雪）和洪水转移，来展示我们的统一算法。