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- W4283767952 abstract "We present a novel U-Attention vision Transformer for universal texture synthesis. We exploit the natural long-range dependencies enabled by the attention mechanism to allow our approach to synthesize diverse textures while preserving their structures in a single inference. We propose a hierarchical hourglass backbone that attends to the global structure and performs patch mapping at varying scales in a coarse-to-fine-to-coarse stream. Completed by skip connection and convolution designs that propagate and fuse information at different scales, our hierarchical U-Attention architecture unifies attention to features from macro structures to micro details, and progressively refines synthesis results at successive stages. Our method achieves stronger 2 × synthesis than previous work on both stochastic and structured textures while generalizing to unseen textures without fine-tuning. Ablation studies demonstrate the effectiveness of each component of our architecture." @default.
- W4283767952 created "2022-07-03" @default.
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- W4283767952 date "2022-12-01" @default.
- W4283767952 modified "2023-10-16" @default.
- W4283767952 title "U-Attention to Textures: Hierarchical Hourglass Vision Transformer for Universal Texture Synthesis" @default.
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- W4283767952 doi "https://doi.org/10.1145/3565516.3565525" @default.
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