We address the problem of scene layout generation for diverse domains such as
images, mobile applications, documents, and 3D objects. Most complex scenes,
natural or human-designed, can be expressed as a meaningful arrangement of
simpler compositional graphical primitives. Generating a new layout or
extending an existing layout requires understanding the relationships between
these primitives. To do this, we propose LayoutTransformer, a novel framework
that leverages self-attention to learn contextual relationships between layout
elements and generate novel layouts in a given domain. Our framework allows us
to generate a new layout either from an empty set or from an initial seed set
of primitives, and can easily scale to support an arbitrary of primitives per
layout. Furthermore, our analyses show that the model is able to automatically
capture the semantic properties of the primitives. We propose simple
improvements in both representation of layout primitives, as well as training
methods to demonstrate competitive performance in very diverse data domains
such as object bounding boxes in natural images(COCO bounding box), documents
(PubLayNet), mobile applications (RICO dataset) as well as 3D shapes
(Part-Net). Code and other materials will be made available at
this https URL

本论文提出了一种新的框架 LayoutTransformer，可以通过自注意力学习布局元素之间的上下文关系，并在给定领域中生成新的布局，并能够在多个不同数据领域中表现出色。