  Cross-modal data retrieval has been the basis of various creative tasks
performed by Artificial Intelligence (AI). One such highly challenging task for
AI is to convert a book into its corresponding movie, which most of the
creative film makers do as of today. In this research, we take the first step
towards it by visualizing the content of a book using its corresponding movie
visuals. Given a set of sentences from a book or even a fan-fiction written in
the same universe, we employ deep learning models to visualize the input by
stitching together relevant frames from the movie. We studied and compared
three different types of setting to match the book with the movie content: (i)
Dialog model: using only the dialog from the movie, (ii) Visual model: using
only the visual content from the movie, and (iii) Hybrid model: using the
dialog and the visual content from the movie. Experiments on the publicly
available MovieBook dataset shows the effectiveness of the proposed models.
