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.
How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling
FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；LMU München（慕尼黑大学）；Institute of AI for Health, Helmholtz Zentrum München（慕尼黑亥姆霍兹中心人工智能健康研究所）；University of Birmingham（伯明翰大学）
Generating Storytelling Images with Rich Chains-of-Reasoning
X-LANCE Lab, School of Computer Science, Shanghai Jiao Tong University, China（X-LANCE实验室，计算机科学学院，上海交通大学，中国）；Shanghai Artificial Intelligence Laboratory, China（上海人工智能实验室，中国）；East China University of Technology, China（东华大学，中国）；University of Texas at Arlington, USA（德克萨斯大学阿灵顿分校，美国）
