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Generating an image from a provided descriptive text is quite a challengingtask because of the difficulty in incorporating perceptual information (objectshapes, colors, and their interactions) along with providing high relevancyrelated to the provided text. Current methods first generate an initiallow-resolution image, which typically has irregular object shapes, colors, andinteraction between objects. This initial image is then improved byconditioning on the text. However, these methods mainly address the problem ofusing text representation efficiently in the refinement of the initiallygenerated image, while the success of this refinement process depends heavilyon the quality of the initially generated image, as pointed out in the DM-GANpaper. Hence, we propose a method to provide good initialized images byincorporating perceptual understanding in the discriminator module. We improvethe perceptual information at the first stage itself, which results insignificant improvement in the final generated image. In this paper, we haveapplied our approach to the novel StackGAN architecture. We then show that theperceptual information included in the initial image is improved while modelingimage distribution at multiple stages. Finally, we generated realisticmulti-colored images conditioned by text. These images have good quality alongwith containing improved basic perceptual information. More importantly, theproposed method can be integrated into the pipeline of other state-of-the-arttext-based-image-generation models to generate initial low-resolution images.We also worked on improving the refinement process in StackGAN by augmentingthe third stage of the generator-discriminator pair in the StackGANarchitecture. Our experimental analysis and comparison with thestate-of-the-art on a large but sparse dataset MS COCO further validate theusefulness of our proposed approach.

PerceptionGAN: Real-world Image Construction from Provided Text through Perceptual Understanding