GANosaic: Mosaic Creation with Generative Texture Manifolds
This work addresses texture synthesis and mosaic creation for computer graphics and image processing applications, representing an incremental improvement with a novel regularization technique.
The paper tackles the problem of generating high-resolution texture mosaics by optimizing in the latent noise space of a generative texture model, resulting in smooth, borderless mosaic images that blend textures locally.
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.