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Semantic Layout Manipulation with High-Resolution Sparse Attention
[article]
2022
arXiv
pre-print
We tackle the problem of semantic image layout manipulation, which aims to manipulate an input image by editing its semantic label map. A core problem of this task is how to transfer visual details from the input images to the new semantic layout while making the resulting image visually realistic. Recent work on learning cross-domain correspondence has shown promising results for global layout transfer with dense attention-based warping. However, this method tends to lose texture details due
arXiv:2012.07288v4
fatcat:3wx7o3z7azgmrigu4o6fygndo4