  Story visualization aims to generate a sequence of images to narrate each
sentence in a multi-sentence story with a global consistency across dynamic
scenes and characters. Current works still struggle with output images' quality
and consistency, and rely on additional semantic information or auxiliary
captioning networks. To address these challenges, we first introduce a new
sentence representation, which incorporates word information from all story
sentences to mitigate the inconsistency problem. Then, we propose a new
discriminator with fusion features and further extend the spatial attention to
improve image quality and story consistency. Extensive experiments on different
datasets and human evaluation demonstrate the superior performance of our
approach, compared to state-of-the-art methods, neither using segmentation
masks nor auxiliary captioning networks.
