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In this paper, we address the task of semantics-guided image outpainting,which is to complete an image by generating semantically practical content.Different from most existing image outpainting works, we approach the abovetask by understanding and completing image semantics at the scene graph level.In particular, we propose a novel network of Scene Graph Transformer (SGT),which is designed to take node and edge features as inputs for modeling theassociated structural information. To better understand and process graph-basedinputs, our SGT uniquely performs feature attention at both node and edgelevels. While the former views edges as relationship regularization, the latterobserves the co-occurrence of nodes for guiding the attention process. Wedemonstrate that, given a partial input image with its layout and scene graph,our SGT can be applied for scene graph expansion and its conversion to acomplete layout. Following state-of-the-art layout-to-image conversions works,the task of image outpainting can be completed with sufficient and practicalsemantics introduced. Extensive experiments are conducted on the datasets ofMS-COCO and Visual Genome, which quantitatively and qualitatively confirm theeffectiveness of our proposed SGT and outpainting frameworks.

Scene Graph Expansion for Semantics-Guided Image Outpainting