  Generating an image from its description is a challenging task worth solving
because of its numerous practical applications ranging from image editing to
virtual reality. All existing methods use one single caption to generate a
plausible image. A single caption by itself, can be limited, and may not be
able to capture the variety of concepts and behavior that may be present in the
image. We propose two deep generative models that generate an image by making
use of multiple captions describing it. This is achieved by ensuring
'Cross-Caption Cycle Consistency' between the multiple captions and the
generated image(s). We report quantitative and qualitative results on the
standard Caltech-UCSD Birds (CUB) and Oxford-102 Flowers datasets to validate
the efficacy of the proposed approach.
