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In this paper, we introduce a novel approach for generating texture images ofinfinite sizes using Generative Adversarial Networks (GANs) based on apatch-by-patch paradigm. Existing texture synthesis techniques rely ongenerating large-scale textures using a single forward pass to the generativemodel; this approach limits the scalability and flexibility of the imagesproduced. In contrast, the proposed approach trains a GAN model on a singletexture image to generate relatively small-size patches that are locallycorrelated and can be seamlessly concatenated to form a larger image. Themethod relies on local padding in the generator to ensure consistency betweenthe generated patches. It also utilizes spatial stochastic modulation to allowfor local variations and improve patterns alignment in the large-scale image.The trained models learn the local texture structure and are able to generateimages of arbitrary sizes, while also maintaining the coherence and diversity.Experimental results demonstrate constant GPU scalability with respect to thegenerated image size compared to existing approaches that exhibit aproportional growth in GPU memory.

Generating Infinite-Size Textures using GANs with Patch-by-Patch Paradigm