Most existing methods focus on improving the clarity and semantic consistency of the image with a given text, but do not pay attention to the multiple control of generated image content, such as the position of the object in generated image. In this paper, we introduce a novel position-based generative network (PBNet) which can generate fine-grained images with the object at the specified location. PBNet combines iterative structure with generative adversarial network (GAN). A location information embedding module (LIEM) is proposed to combine the location information extracted from the boundary block image with the semantic information extracted from the text. In addition, a silhouette generation module (SGM) is proposed to train the generator to generate object based on location information. The experimental results on CUB dataset demonstrate that PBNet effectively controls the location of the object in the generated image.

Most existing text-to-image generation methods focus on synthesizing images using only text descriptions, but this cannot meet the requirement of generating desired objects with given backgrounds. In this paper, we propose a Background-induced Generative Network (BGNet) that combines attention mechanisms, background synthesis, and multi-level discriminator to generate realistic images with given backgrounds according to text descriptions. BGNet takes a multi-stage generation as the basic framework to generate fine-grained images and introduces a hybrid attention mechanism to capture the local semantic correlation between texts and images. To adjust the impact of the given backgrounds on the synthesized images, synthesis blocks are added at each stage of image generation, which appropriately combines the foreground objects generated by the text descriptions with the given background images. Besides, a multi-level discriminator and its corresponding loss function are proposed to optimize the synthesized images. The experimental results on the CUB bird dataset demonstrate the superiority of our method and its ability to generate realistic images with given backgrounds.