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.

提出利用语义布局来综合生成逼真图像的新方法，该方法建立了区域之间的依赖关系并通过动态加权网络实现，既保留了语义区分，同时加强了语义相关性，提高了全局结构和细节的合成质量。

语义合成图像

Image Synthesis via Semantic Composition

We present the first neural video compression method based on generative
adversarial networks (GANs). Our approach significantly outperforms previous
neural and non-neural video compression methods in a user study, setting a new
state-of-the-art in visual quality for neural methods. We show that the GAN
loss is crucial to obtain this high visual quality. Two components make the GAN
loss effective: we i) synthesize detail by conditioning the generator on a
latent extracted from the warped previous reconstruction to then ii) propagate
this detail with high-quality flow. We find that user studies are required to
compare methods, i.e., none of our quantitative metrics were able to predict
all studies. We present the network design choices in detail, and ablate them
with user studies.

我们提出了一种基于生成对抗网络（GANs）的神经视频压缩方法，它在用户研究中明显优于以前的神经和非神经视频压缩方法，并为神经方法的视觉质量设定了新的最新技术。我们表明 GAN 损失对于获得这种高视觉质量是至关重要的，并通过用户研究验证了这种方法的有效性。