  Synthesizing photo-realistic images from text descriptions is a challenging
problem. Previous studies have shown remarkable progresses on visual quality of
the generated images. In this paper, we consider semantics from the input text
descriptions in helping render photo-realistic images. However, diverse
linguistic expressions pose challenges in extracting consistent semantics even
they depict the same thing. To this end, we propose a novel photo-realistic
text-to-image generation model that implicitly disentangles semantics to both
fulfill the high-level semantic consistency and low-level semantic diversity.
To be specific, we design (1) a Siamese mechanism in the discriminator to learn
consistent high-level semantics, and (2) a visual-semantic embedding strategy
by semantic-conditioned batch normalization to find diverse low-level
semantics. Extensive experiments and ablation studies on CUB and MS-COCO
datasets demonstrate the superiority of the proposed method in comparison to
state-of-the-art methods.
