This paper presents a novel framework for generating texture mosaics with convolutional neural networks. Our method is called GANosaic and performs optimization in the latent noise space of a generative texture model, which allows the transformation of a content image into a mosaic exhibiting the visual properties of the underlying texture manifold. To represent that manifold, we use a state-of-the-art generative adversarial method for texture synthesis, which can learn expressive texture representations from data and produce mosaic images with very high resolution. This fully convolutional model generates smooth (without any visible borders) mosaic images which morph and blend different textures locally. In addition, we develop a new type of differentiable statistical regularization appropriate for optimization over the prior noise space of the PSGAN model.

本文提出了一种名为GANosaic的基于卷积神经网络的纹理马赛克生成框架，该框架可以将内容图像转换为具有所需纹理特性的马赛克图像。该方法利用了一种基于生成对抗方法的纹理合成技术来表示纹理 manifold 并生成平滑的无可见边界的马赛克图像，并且该方法添加了适用于PSGAN模型先验噪声空间优化的新型可微统计正则化。

GANosaic: 生成纹理流形实现马赛克创作