# A Pipeline for Creative Visual Storytelling

arXiv: https://arxiv.org/abs/1807.08077 · HTML: https://mlsanity.com/paper/1807.08077/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2018.

## Built on by (most cited first, 7 of 7)

- [Holistic Evaluation for Interleaved Text-and-Image Generation](https://mlsanity.com/paper/2406.14643/) (2024)
- [Envisioning Narrative Intelligence: A Creative Visual Storytelling Anthology](https://mlsanity.com/paper/2310.04529/) (2023)
- [HL Dataset: Visually-grounded Description of Scenes, Actions and Rationales](https://mlsanity.com/paper/2302.12189/) (2023)
- [SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus](https://mlsanity.com/paper/2411.12844/) (2024)
- [Multi-Modal Experience Inspired AI Creation](https://mlsanity.com/paper/2209.02427/) (2022)
- [Story Generation from Visual Inputs: Techniques, Related Tasks, and Challenges](https://mlsanity.com/paper/2406.02748/) (2024)
- [EVOQUER: Enhancing Temporal Grounding with Video-Pivoted BackQuery Generation](https://mlsanity.com/paper/2109.04600/) (2021)

## Abstract (arXiv)

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.