  This paper addresses the problem of manipulating images using natural
language description. Our task aims to semantically modify visual attributes of
an object in an image according to the text describing the new visual
appearance. Although existing methods synthesize images having new attributes,
they do not fully preserve text-irrelevant contents of the original image. In
this paper, we propose the text-adaptive generative adversarial network (TAGAN)
to generate semantically manipulated images while preserving text-irrelevant
contents. The key to our method is the text-adaptive discriminator that creates
word-level local discriminators according to input text to classify
fine-grained attributes independently. With this discriminator, the generator
learns to generate images where only regions that correspond to the given text
are modified. Experimental results show that our method outperforms existing
methods on CUB and Oxford-102 datasets, and our results were mostly preferred
on a user study. Extensive analysis shows that our method is able to
effectively disentangle visual attributes and produce pleasing outputs.
