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Text to image generation methods (T2I) are widely popular in generating artand other creative artifacts. While visual hallucinations can be a positivefactor in scenarios where creativity is appreciated, such artifacts are poorlysuited for cases where the generated image needs to be grounded in complexnatural language without explicit visual elements. In this paper, we propose tostrengthen the consistency property of T2I methods in the presence of naturalcomplex language, which often breaks the limits of T2I methods by includingnon-visual information, and textual elements that require knowledge foraccurate generation. To address these phenomena, we propose a Natural Languageto Verified Image generation approach (NL2VI) that converts a natural promptinto a visual prompt, which is more suitable for image generation. A T2I modelthen generates an image for the visual prompt, which is then verified with VQAalgorithms. Experimentally, aligning natural prompts with image generation canimprove the consistency of the generated images by up to 11% over the state ofthe art. Moreover, improvements can generalize to challenging domains likecooking and DIY tasks, where the correctness of the generated image is crucialto illustrate actions.

Transferring Visual Attributes from Natural Language to Verified Image Generation