  In this paper, we introduce a new method for generating an object image from
text attributes on a desired location, when the base image is given. One step
further to the existing studies on text-to-image generation mainly focusing on
the object's appearance, the proposed method aims to generate an object image
preserving the given background information, which is the first attempt in this
field. To tackle the problem, we propose a multi-conditional GAN (MC-GAN) which
controls both the object and background information jointly. As a core
component of MC-GAN, we propose a synthesis block which disentangles the object
and background information in the training stage. This block enables MC-GAN to
generate a realistic object image with the desired background by controlling
the amount of the background information from the given base image using the
foreground information from the text attributes. From the experiments with
Caltech-200 bird and Oxford-102 flower datasets, we show that our model is able
to generate photo-realistic images with a resolution of 128 x 128. The source
code of MC-GAN is released.
