  Text-to-image generation aims at generating realistic images which are
semantically consistent with the given text. Previous works mainly adopt the
multi-stage architecture by stacking generator-discriminator pairs to engage
multiple adversarial training, where the text semantics used to provide
generation guidance remain static across all stages. This work argues that text
features at each stage should be adaptively re-composed conditioned on the
status of the historical stage (i.e., historical stage's text and image
features) to provide diversified and accurate semantic guidance during the
coarse-to-fine generation process. We thereby propose a novel Dynamical
Semantic Evolution GAN (DSE-GAN) to re-compose each stage's text features under
a novel single adversarial multi-stage architecture. Specifically, we design
(1) Dynamic Semantic Evolution (DSE) module, which first aggregates historical
image features to summarize the generative feedback, and then dynamically
selects words required to be re-composed at each stage as well as re-composed
them by dynamically enhancing or suppressing different granularity subspace's
semantics. (2) Single Adversarial Multi-stage Architecture (SAMA), which
extends the previous structure by eliminating complicated multiple adversarial
training requirements and therefore allows more stages of text-image
interactions, and finally facilitates the DSE module. We conduct comprehensive
experiments and show that DSE-GAN achieves 7.48\% and 37.8\% relative FID
improvement on two widely used benchmarks, i.e., CUB-200 and MSCOCO,
respectively.
