  Synthesizing images from a given text description involves engaging two types
of information: the content, which includes information explicitly described in
the text (e.g., color, composition, etc.), and the style, which is usually not
well described in the text (e.g., location, quantity, size, etc.). However, in
previous works, it is typically treated as a process of generating images only
from the content, i.e., without considering learning meaningful style
representations. In this paper, we aim to learn two variables that are
disentangled in the latent space, representing content and style respectively.
We achieve this by augmenting current text-to-image synthesis frameworks with a
dual adversarial inference mechanism. Through extensive experiments, we show
that our model learns, in an unsupervised manner, style representations
corresponding to certain meaningful information present in the image that are
not well described in the text. The new framework also improves the quality of
synthesized images when evaluated on Oxford-102, CUB and COCO datasets.
