Generating Non-Stationary Textures Using Self-Rectification

This paper addresses the challenge of example-based non-stationary texture synthesis. We introduce a novel two-step 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 pretrained diffusion network, and uses self-attention mechanisms, to grad-ually align the synthesized texture with the reference, en-suring the retention of the structures in the provided target. Through experimental validation, our approach ex-hibits 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