Interactive image synthesis from user-guided input is a challenging task when
users wish to control the scene structure of a generated image with
ease.Although remarkable progress has been made on layout-based image synthesis
approaches, in order to get realistic fake image in interactive scene, existing
methods require high-precision inputs, which probably need adjustment several
times and are unfriendly to novice users. When placement of bounding boxes is
subject to perturbation, layout-based models suffer from "missing regions" in
the constructed semantic layouts and hence undesirable artifacts in the
generated images. In this work, we propose Panoptic Layout Generative
Adversarial Networks (PLGAN) to address this challenge. The PLGAN employs
panoptic theory which distinguishes object categories between "stuff" with
amorphous boundaries and "things" with well-defined shapes, such that stuff and
instance layouts are constructed through separate branches and later fused into
panoptic layouts. In particular, the stuff layouts can take amorphous shapes
and fill up the missing regions left out by the instance layouts. We
experimentally compare our PLGAN with state-of-the-art layout-based models on
the COCO-Stuff, Visual Genome, and Landscape datasets. The advantages of PLGAN
are not only visually demonstrated but quantitatively verified in terms of
inception score, Fr\'echet inception distance, classification accuracy score,
and coverage.

本文提出了 Panoptic Layout Generative Adversarial Networks（PLGAN）来解决交互式场景中实现真实感的假图像生成难题，该方法使用 panoptic 理论根据物体的形状判断物体种类，将 stuff 和 instance 布局分别构建后再融合为 panoptic 布局，实验结果表明 PLGAN 具有明显优势。

通过全景布局生成交互式图像合成

Interactive Image Synthesis with Panoptic Layout Generation

In computer vision, convolutional neural networks (CNNs) have recently
achieved new levels of performance for several inverse problems where RGB pixel
appearance is mapped to attributes such as positions, normals or reflectance.
In computer graphics, screen-space shading has recently increased the visual
quality in interactive image synthesis, where per-pixel attributes such as
positions, normals or reflectance of a virtual 3D scene are converted into RGB
pixel appearance, enabling effects like ambient occlusion, indirect light,
scattering, depth-of-field, motion blur, or anti-aliasing. In this paper we
consider the diagonal problem: synthesizing appearance from given per-pixel
attributes using a CNN. The resulting Deep Shading simulates various
screen-space effects at competitive quality and speed while not being
programmed by human experts but learned from example images.

本文介绍了一种基于卷积神经网络的 Deep Shading 方法，通过学习示例图像生成给定像素属性的外观，模拟了屏幕空间中的各种影响，提高了交互式图像合成的视觉质量和速度。