Layout and context understanding for image synthesis with scene graphs

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

Proceedings - International Conference on Image Processing, ICIP

Volume

2019-September

First Page

1905

Last Page

1909

Publication Date

9-1-2019

Abstract

Advancements on text-to-image synthesis generate remarkable images from textual descriptions. However, these methods are designed to generate only one object with varying attributes. They face difficulties with complex descriptions having multiple arbitrary objects since it would require information on the placement and sizes of each object in the image. Recently, a method that infers object layouts from scene graphs has been proposed as a solution to this problem. However, their method uses only object labels in describing the layout, which fail to capture the appearance of some objects. Moreover, their model is biased towards generating rectangular shaped objects in the absence of ground-truth masks. In this paper, we propose an object encoding module to capture object features and use it as additional information to the image generation network. We also introduce a graph-cuts based segmentation method that can infer the masks of objects from bounding boxes to better model object shapes. Our method produces more discernible images with more realistic shapes as compared to the images generated by the current state-of-the-art method. © 2019 IEEE.

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Digitial Object Identifier (DOI)

10.1109/ICIP.2019.8803182

Disciplines

Computer Sciences

Keywords

Image analysis; Computer graphics

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