Abstract We propose an end-to-end network for visual illustration of a sequence of sentences forming a story.At the core of our model is the ability to model the inter-related na- ture of the sentences within a story,as well as the ability to learn coherence to support reference resolution.The frame- work takes the form of an encoder-decoder architecture, where sentences are encoded using a hierarchical two-level sentence-story GRU,combined with an encoding of coher- ence,and sequentially decoded using a predicted feature representation into a consistent illustrative image sequence. We optimize all parameters of our network in an end-to- end fashion with respect to order embedding loss,encoding entailment between images and sentences.Experiments on the VIST storytelling dataset[9]highlight the importance of our algorithmic choices and efficacy of our overall model