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Translating information between text and image is a fundamental problem inartificial intelligence that connects natural language processing and computervision. In the past few years, performance in image caption generation has seensignificant improvement through the adoption of recurrent neural networks(RNN). Meanwhile, text-to-image generation begun to generate plausible imagesusing datasets of specific categories like birds and flowers. We've even seenimage generation from multi-category datasets such as the Microsoft CommonObjects in Context (MSCOCO) through the use of generative adversarial networks(GANs). Synthesizing objects with a complex shape, however, is stillchallenging. For example, animals and humans have many degrees of freedom,which means that they can take on many complex shapes. We propose a newtraining method called Image-Text-Image (I2T2I) which integrates text-to-imageand image-to-text (image captioning) synthesis to improve the performance oftext-to-image synthesis. We demonstrate that %the capability of our method tounderstand the sentence descriptions, so as to I2T2I can generate bettermulti-categories images using MSCOCO than the state-of-the-art. We alsodemonstrate that I2T2I can achieve transfer learning by using a pre-trainedimage captioning module to generate human images on the MPII Human Pose

I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation