Envisioning Narrative Intelligence: A Creative Visual Storytelling Anthology
This work addresses the challenge of generating more human-like and creative stories from images for AI and storytelling applications, but it is incremental as it builds on existing data collection and analysis methods without introducing new computational techniques.
The paper tackles the problem of understanding how people create stories from images by collecting and analyzing 100 visual stories, identifying five themes that characterize variations in this creative process. It proposes narrative intelligence criteria for computational visual storytelling to inform automatic story generation.
In this paper, we collect an anthology of 100 visual stories from authors who participated in our systematic creative process of improvised story-building based on image sequences. Following close reading and thematic analysis of our anthology, we present five themes that characterize the variations found in this creative visual storytelling process: (1) Narrating What is in Vision vs. Envisioning; (2) Dynamically Characterizing Entities/Objects; (3) Sensing Experiential Information About the Scenery; (4) Modulating the Mood; (5) Encoding Narrative Biases. In understanding the varied ways that people derive stories from images, we offer considerations for collecting story-driven training data to inform automatic story generation. In correspondence with each theme, we envision narrative intelligence criteria for computational visual storytelling as: creative, reliable, expressive, grounded, and responsible. From these criteria, we discuss how to foreground creative expression, account for biases, and operate in the bounds of visual storyworlds.