This paper addresses the challenge of example-based non-stationary texture
synthesis. We introduce a novel twostep approach wherein users first modify a
reference texture using standard image editing tools, yielding an initial rough
target for the synthesis. Subsequently, our proposed method, termed
"self-rectification", automatically refines this target into a coherent,
seamless texture, while faithfully preserving the distinct visual
characteristics of the reference exemplar. Our method leverages a pre-trained
diffusion network, and uses self-attention mechanisms, to gradually align the
synthesized texture with the reference, ensuring the retention of the
structures in the provided target. Through experimental validation, our
approach exhibits exceptional proficiency in handling non-stationary textures,
demonstrating significant advancements in texture synthesis when compared to
existing state-of-the-art techniques. Code is available at
this https URL

本文提出了一种处理非稳定纹理合成的基于示例的方法，通过引入一种新的两步方法，用户首先使用标准图像编辑工具修改参考纹理，得到一个初步的目标合成图，随后采用自我校正的方法，利用预先训练的扩散网络和自注意力机制，渐进地将目标合成图与参考图进行对齐，从而保留所提供目标中的结构，并在非稳定纹理处理上取得了显著的进展。