  Computational visual storytelling produces a textual description of events
and interpretations depicted in a sequence of images. These texts are made
possible by advances and cross-disciplinary approaches in natural language
processing, generation, and computer vision. We define a computational creative
visual storytelling as one with the ability to alter the telling of a story
along three aspects: to speak about different environments, to produce
variations based on narrative goals, and to adapt the narrative to the
audience. These aspects of creative storytelling and their effect on the
narrative have yet to be explored in visual storytelling. This paper presents a
pipeline of task-modules, Object Identification, Single-Image Inferencing, and
Multi-Image Narration, that serve as a preliminary design for building a
creative visual storyteller. We have piloted this design for a sequence of
images in an annotation task. We present and analyze the collected corpus and
describe plans towards automation.
