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 https://github.com/xiaorongjun000/Self-Rectification

翻译：本文针对基于示例的非平稳纹理合成这一挑战性问题展开研究。我们提出了一种新颖的两步法：用户首先利用标准图像编辑工具对参考纹理进行修改，从而为合成过程生成初始粗糙目标；随后，我们提出的"自校正"方法会自动将该目标优化为连贯无缝的纹理，同时忠实地保留参考样本的独特视觉特征。该方法借助预训练扩散网络，并运用自注意力机制，逐步使合成纹理与参考纹理对齐，确保保留所提供目标中的结构。通过实验验证，本方法在处理非平稳纹理方面表现出色，与现有最先进技术相比，在纹理合成领域展现了显著进步。代码开源地址：https://github.com/xiaorongjun000/Self-Rectification