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We present a novel U-Attention vision Transformer for universal texturesynthesis. We exploit the natural long-range dependencies enabled by theattention mechanism to allow our approach to synthesize diverse textures whilepreserving their structures in a single inference. We propose a hierarchicalhourglass backbone that attends to the global structure and performs patchmapping at varying scales in a coarse-to-fine-to-coarse stream. Completed byskip connection and convolution designs that propagate and fuse information atdifferent scales, our hierarchical U-Attention architecture unifies attentionto features from macro structures to micro details, and progressively refinessynthesis results at successive stages. Our method achieves stronger 2$\times$synthesis than previous work on both stochastic and structured textures whilegeneralizing to unseen textures without fine-tuning. Ablation studiesdemonstrate the effectiveness of each component of our architecture.

Paying U-Attention to Textures: Multi-Stage Hourglass Vision Transformer for Universal Texture Synthesis