Layout-Bridging Text-to-Image Synthesis

TL;DR

This paper introduces a novel approach for text-to-image synthesis that emphasizes effective layout modeling through Transformer-based text-to-layout generation and layout-to-image synthesis, improving semantic consistency and spatial accuracy.

Contribution

It proposes a new Transformer-based framework for joint text-to-layout and layout-to-image synthesis, along with a novel Layout Quality Score metric for evaluating layout quality.

Findings

Outperforms state-of-the-art methods in layout prediction

Achieves higher image synthesis quality from text descriptions

Demonstrates effective modeling of spatial relationships

Abstract

The crux of text-to-image synthesis stems from the difficulty of preserving the cross-modality semantic consistency between the input text and the synthesized image. Typical methods, which seek to model the text-to-image mapping directly, could only capture keywords in the text that indicates common objects or actions but fail to learn their spatial distribution patterns. An effective way to circumvent this limitation is to generate an image layout as guidance, which is attempted by a few methods. Nevertheless, these methods fail to generate practically effective layouts due to the diversity of input text and object location. In this paper we push for effective modeling in both text-to-layout generation and layout-to-image synthesis. Specifically, we formulate the text-to-layout generation as a sequence-to-sequence modeling task, and build our model upon Transformer to learn the spatial relationships between objects by modeling the sequential dependencies between them. In the stage of layout-to-image synthesis, we focus on learning the textual-visual semantic alignment per object in the layout to precisely incorporate the input text into the layout-to-image synthesizing process. To evaluate the quality of generated layout, we design a new metric specifically, dubbed Layout Quality Score, which considers both the absolute distribution errors of bounding boxes in the layout and the mutual spatial relationships between them. Extensive experiments on three datasets demonstrate the superior performance of our method over state-of-the-art methods on both predicting the layout and synthesizing the image from the given text.