  Text-to-image synthesis (T2I) aims to generate photo-realistic images which
are semantically consistent with the text descriptions. Existing methods are
usually built upon conditional generative adversarial networks (GANs) and
initialize an image from noise with sentence embedding, and then refine the
features with fine-grained word embedding iteratively. A close inspection of
their generated images reveals a major limitation: even though the generated
image holistically matches the description, individual image regions or parts
of somethings are often not recognizable or consistent with words in the
sentence, e.g. "a white crown". To address this problem, we propose a novel
framework Semantic-Spatial Aware GAN for synthesizing images from input text.
Concretely, we introduce a simple and effective Semantic-Spatial Aware block,
which (1) learns semantic-adaptive transformation conditioned on text to
effectively fuse text features and image features, and (2) learns a semantic
mask in a weakly-supervised way that depends on the current text-image fusion
process in order to guide the transformation spatially. Experiments on the
challenging COCO and CUB bird datasets demonstrate the advantage of our method
over the recent state-of-the-art approaches, regarding both visual fidelity and
alignment with input text description.
