Simply by rearranging the regions of an image, we can create a new image of any subject matter. The definition of regions is user definable, ranging from regularly and irregularly-shaped blocks, concentric rings, or even individual pixels. Our method extends and improves recent work in the generation of optical illusions by simultaneously learning not only the content of the images, but also the parameterized transformations required to transform the desired images into each other. By learning the image transforms, we allow any source image to be pre-specified; any existing image (e.g. the Mona Lisa) can be transformed to a novel subject. We formulate this process as a constrained optimization problem and address it through interleaving the steps of image diffusion with an energy minimization step. Unlike previous methods, increasing the number of regions actually makes the problem easier and improves results. We demonstrate our approach in both pixel and latent spaces. Creative extensions, such as using infinite copies of the source image and employing multiple source images, are also given.

翻译：仅通过重新排列图像的各个区域，我们便能生成任意主题的新图像。区域的定义可由用户自定义，范围涵盖规则或不规则形状的区块、同心圆环，甚至单个像素。我们的方法扩展并改进了近期在光学幻觉生成方面的研究，不仅同时学习图像内容，还学习将目标图像相互转换所需的参数化变换。通过学习图像变换，我们允许任意源图像被预先指定；任何现有图像（如《蒙娜丽莎》）均可转换为新颖的主题。我们将此过程表述为约束优化问题，并通过将图像扩散步骤与能量最小化步骤交错进行来解决该问题。与先前方法不同，增加区域数量实际上使问题更易处理并改善了结果。我们在像素空间和潜在空间中均验证了该方法。此外，还提出了创造性扩展，例如使用源图像的无限副本以及采用多源图像。