ﻻ يوجد ملخص باللغة العربية
In this paper, we present a novel approach to synthesize realistic images based on their semantic layouts. It hypothesizes that for objects with similar appearance, they share similar representation. Our method establishes dependencies between regions according to their appearance correlation, yielding both spatially variant and associated representations. Conditioning on these features, we propose a dynamic weighted network constructed by spatially conditional computation (with both convolution and normalization). More than preserving semantic distinctions, the given dynamic network strengthens semantic relevance, benefiting global structure and detail synthesis. We demonstrate that our method gives the compelling generation performance qualitatively and quantitatively with extensive experiments on benchmarks.
Spatially-adaptive normalization (SPADE) is remarkably successful recently in conditional semantic image synthesis cite{park2019semantic}, which modulates the normalized activation with spatially-varying transformations learned from semantic layouts,
The virtual try-on task is so attractive that it has drawn considerable attention in the field of computer vision. However, presenting the three-dimensional (3D) physical characteristic (e.g., pleat and shadow) based on a 2D image is very challenging
We propose a novel Edge guided Generative Adversarial Network (EdgeGAN) for photo-realistic image synthesis from semantic layouts. Although considerable improvement has been achieved, the quality of synthesized images is far from satisfactory due to
We propose semantic region-adaptive normalization (SEAN), a simple but effective building block for Generative Adversarial Networks conditioned on segmentation masks that describe the semantic regions in the desired output image. Using SEAN normaliza
Example-guided image synthesis aims to synthesize an image from a semantic label map and an exemplary image indicating style. We use the term style in this problem to refer to implicit characteristics of images, for example: in portraits style includ