  The goal of our paper is to semantically edit parts of an image matching a
given text that describes desired attributes (e.g., texture, colour, and
background), while preserving other contents that are irrelevant to the text.
To achieve this, we propose a novel generative adversarial network (ManiGAN),
which contains two key components: text-image affine combination module (ACM)
and detail correction module (DCM). The ACM selects image regions relevant to
the given text and then correlates the regions with corresponding semantic
words for effective manipulation. Meanwhile, it encodes original image features
to help reconstruct text-irrelevant contents. The DCM rectifies mismatched
attributes and completes missing contents of the synthetic image. Finally, we
suggest a new metric for evaluating image manipulation results, in terms of
both the generation of new attributes and the reconstruction of text-irrelevant
contents. Extensive experiments on the CUB and COCO datasets demonstrate the
superior performance of the proposed method. Code is available at
https://github.com/mrlibw/ManiGAN.
