  This paper presents a novel method to deal with the challenging task of
generating photographic images conditioned on semantic image descriptions. Our
method introduces accompanying hierarchical-nested adversarial objectives
inside the network hierarchies, which regularize mid-level representations and
assist generator training to capture the complex image statistics. We present
an extensile single-stream generator architecture to better adapt the jointed
discriminators and push generated images up to high resolutions. We adopt a
multi-purpose adversarial loss to encourage more effective image and text
information usage in order to improve the semantic consistency and image
fidelity simultaneously. Furthermore, we introduce a new visual-semantic
similarity measure to evaluate the semantic consistency of generated images.
With extensive experimental validation on three public datasets, our method
significantly improves previous state of the arts on all datasets over
different evaluation metrics.
