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 to the resolution limitation and the lack of
smoothness constraint of correspondence. To adapt this paradigm for the layout
manipulation task, we propose a high-resolution sparse attention module that
effectively transfers visual details to new layouts at a resolution up to
512x512. To further improve visual quality, we introduce a novel generator
architecture consisting of a semantic encoder and a two-stage decoder for
coarse-to-fine synthesis. Experiments on the ADE20k and Places365 datasets
demonstrate that our proposed approach achieves substantial improvements over
the existing inpainting and layout manipulation methods.

该研究提出了一种高分辨率稀疏注意力模块和一个生成器架构，可以在语义标签映射的基础上，有效地实现将输入图像的视觉细节转移至新布局。实验证明，该方法在图像修补和布局操作方面的性能得到了大幅提高。