  Image compositing is one of the most fundamental steps in creative workflows.
It involves taking objects/parts of several images to create a new image,
called a composite. Currently, this process is done manually by creating
accurate masks of objects to be inserted and carefully blending them with the
target scene or images, usually with the help of tools such as Photoshop or
GIMP. While there have been several works on automatic selection of objects for
creating masks, the problem of object placement within an image with the
correct position, scale, and harmony remains a difficult problem with limited
exploration. Automatic object insertion in images or designs is a difficult
problem as it requires understanding of the scene geometry and the color
harmony between objects. We propose LayoutBERT for the object insertion task.
It uses a novel self-supervised masked language model objective and
bidirectional multi-head self-attention. It outperforms previous layout-based
likelihood models and shows favorable properties in terms of model capacity. We
demonstrate the effectiveness of our approach for object insertion in the image
compositing setting and other settings like documents and design templates. We
further demonstrate the usefulness of the learned representations for
layout-based retrieval tasks. We provide both qualitative and quantitative
evaluations on datasets from diverse domains like COCO, PublayNet, and two new
datasets which we call Image Layouts and Template Layouts. Image Layouts which
consists of 5.8 million images with layout annotations is the largest image
layout dataset to our knowledge. We also share ablation study results on the
effect of dataset size, model size and class sample size for this task.
