In this paper, we introduce a novel approach for generating texture images of infinite resolutions using Generative Adversarial Networks (GANs) based on a patch-by-patch paradigm. Existing texture synthesis techniques often rely on generating a large-scale texture using a one-forward pass to the generating model, this limits the scalability and flexibility of the generated images. In contrast, the proposed approach trains GANs models on a single texture image to generate relatively small patches that are locally correlated and can be seamlessly concatenated to form a larger image while using a constant GPU memory footprint. Our method learns the local texture structure and is able to generate arbitrary-size textures, while also maintaining coherence and diversity. The proposed method relies on local padding in the generator to ensure consistency between patches and utilizes spatial stochastic modulation to allow for local variations and diversity within the large-scale image. Experimental results demonstrate superior scalability compared to existing approaches while maintaining visual coherence of generated textures.

翻译：本文提出了一种基于生成对抗网络（GANs）和逐块范式的新方法，用于生成无限分辨率的纹理图像。现有纹理合成技术通常依赖对生成模型进行一次前向传递来生成大尺度纹理，这限制了生成图像的可扩展性和灵活性。相比之下，所提方法在单张纹理图像上训练GAN模型，生成局部相关的较小图像块，这些块可无缝拼接成更大图像，同时保持恒定的GPU内存占用。该方法学习局部纹理结构，能够生成任意尺寸的纹理，同时保持连贯性和多样性。该方法在生成器中采用局部填充策略确保块间一致性，并利用空间随机调制实现大尺度图像内的局部变化与多样性。实验结果表明，与现有方法相比，本方法在保持生成纹理视觉连贯性的同时展现出更优越的可扩展性。