Vision is a common source of inspiration for poetry. The objects and the sentimental imprints that one perceives from an image may lead to various feelings depending on the reader. In this paper, we present a system of poetry generation from images to mimic the process. Given an image, we first extract a few keywords representing objects and sentiments perceived from the image. These keywords are then expanded to related ones based on their associations in human written poems. Finally, verses are generated gradually from the keywords using recurrent neural networks trained on existing poems. Our approach is evaluated by human assessors and compared to other generation baselines. The results show that our method can generate poems that are more artistic than the baseline methods. This is one of the few attempts to generate poetry from images. By deploying our proposed approach, XiaoIce has already generated more than 12 million poems for users since its release in July 2017. A book of its poems has been published by Cheers Publishing, which claimed that the book is the first-ever poetry collection written by an AI in human history.
We develop an optimization based framework to automatically illustrate poems and songs. Our method is able to produce both semantically relevant and visually coherent illustrations, all while matching a particular user selected visual style. We demonstrate our method on a selection of 200 popular poems and songs collected from the internet and operate on around 14M Flickr images. A user study evaluates variations on our optimization procedure. Finally, we present two applications, identifying textual style, and automatic music video generation.