End-to-End Text-to-Image Synthesis with Spatial Constrains

Although the performance of automatically generating high-resolution realistic images from text descriptions has been significantly boosted, many challenging issues in image synthesis have not been fully investigated, due to shapes variations, viewpoint changes, pose changes, and the relations of multiple objects. In this article, we propose a novel end-to-end approach for text-to-image synthesis with spatial constraints by mining object spatial location and shape information. Instead of learning a hierarchical mapping from text to image, our algorithm directly generates multi-object fine-grained images through the guidance of the generated semantic layouts. By fusing text semantic and spatial information into a synthesis module and jointly fine-tuning them with multi-scale semantic layouts generated, the proposed networks show impressive performance in text-to-image synthesis for complex scenes. We evaluate our method both on single-object CUB dataset and multi-object MS-COCO dataset. Comprehensive experimental results demonstrate that our method significantly outperforms the state-of-the-art approaches consistently across different evaluation metrics.