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