Collaborative Comic Generation: Integrating Visual Narrative Theories with AI Models for Enhanced Creativity
North Carolina State University（北卡罗来纳州立大学）
Yi-Chun Chen, Arnav Jhala
备注：
This paper has been accepted for oral presentation at CREAI2024, ECAI, 2024. However, the author's attendance is currently uncertain due to visa issues
This study presents a theory-inspired visual narrative generative system that integrates conceptual principles-comic authoring idioms-with generative and language models to enhance the comic creation process. Our system combines human creativity with AI models to support parts of the generative process, providing a collaborative platform for creating comic content. These comic-authoring idioms, derived from prior human-created image sequences, serve as guidelines for crafting and refining storytelling. The system translates these principles into system layers that facilitate comic creation through sequential decision-making, addressing narrative elements such as panel composition, story tension changes, and panel transitions. Key contributions include integrating machine learning models into the human-AI cooperative comic generation process, deploying abstract narrative theories into AI-driven comic creation, and a customizable tool for narrative-driven image sequences. This approach improves narrative elements in generated image sequences and engages human creativity in an AI-generative process of comics. We open-source the code at https://github.com/RimiChen/Collaborative_Comic_Generation.
This papers revisits the receptive theory in context of computational creativity. It presents a case study of a Paranoid Transformer - a fully autonomous text generation engine with raw output that could be read as the narrative of a mad digital persona without any additional human post-filtering. We describe technical details of the generative system, provide examples of output and discuss the impact of receptive theory, chance discovery and simulation of fringe mental state on the understanding of computational creativity.
Zooming into Comics: Region-Aware RL Improves Fine-Grained Comic Understanding in Vision-Language Models
School of Computer and Communication Sciences, EPFL（瑞士联邦理工学院计算机与通信科学学院）；Department of Computer Science and Engineering, Chalmers University of Technology（楚科奇技术大学计算机科学与工程系）；IVRL, EPFL（EPFL智能机器人实验室）
University of Southern California（南加州大学）；Tokyo University（东京大学）；University of California, Los Angeles（加州大学洛杉矶分校）
Stories or narratives are comprised of a sequence of events. To compose interesting stories, professional writers often leverage a creative writing technique called flashback that inserts past events into current storylines as we commonly observe in novels and plays. However, it is challenging for machines to generate flashback as it requires a solid understanding of event temporal order (e.g. "feeling hungry" before "eat," not vice versa), and the creativity to arrange storylines so that earlier events do not always appear first in narrative order. Two major issues in existing systems that ex…
Is Temperature the Creativity Parameter of Large Language Models?
School of Computing, University of Kent（肯特大学计算机学院）；Leiden Institute of Advanced Computer Science, Universiteit Leiden（莱顿大学高级计算机科学研究所）；Cheriton School of Computer Science, University of Waterloo（滑铁卢大学谢里敦计算机学院）
Instituto de Telecomunicações (IT-IUL)（电信研究所（IT-IUL））；Instituto Universitário de Lisboa (Iscte-IUL)（里斯本大学学院（Iscte-IUL））
We present a literature survey on non-interactive computational story generation. The article starts with the presentation of requirements for creative systems, three types of models of creativity (computational, socio-cultural, and individual), and models of human creative writing. Then it reviews each class of story generation approach depending on the used technology: story-schemas, analogy, rules, planning, evolutionary algorithms, implicit knowledge learning, and explicit knowledge learning. Before the concluding section, the article analyses the contributions of the reviewed work to impr…
MindFuse: Towards GenAI Explainability in Marketing Strategy Co-Creation
ITMO University（ITMO大学）；SoMin.ai Research（SoMin.ai研究）；Faculty of Software Engineering（软件工程学院）；Computer Systems, ITMO University（计算机系统，ITMO大学）；ISP RAS Research Center for Trusted Artificial Intelligence（俄罗斯科学院信息与系统研究所以及可信人工智能研究中心）
University of Hertfordshire（赫特福德大学）；Queen Mary, University of London（伦敦大学玛丽皇后学院）；New York University（纽约大学）
We introduce the Chronicle Challenge as an optional addition to the Settlement Generation Challenge in Minecraft. One of the foci of the overall competition is adaptive procedural content generation (PCG), an arguably under-explored problem in computational creativity. In the base challenge, participants must generate new settlements that respond to and ideally interact with existing content in the world, such as the landscape or climate. The goal is to understand the underlying creative process, and to design better PCG systems. The Chronicle Challenge in particular focuses on the generation …
What lies between `\emph{sensing}' and `\emph{sensibility}'? In other words, what kind of cognitive processes mediate sensing capability, and the formation of sensible impressions ---e.g., abstractions, analogies, hypotheses and theory formation, beliefs and their revision, argument formation--- in domain-specific problem solving, or in regular activities of everyday living, working and simply going around in the environment? How can knowledge and reasoning about such capabilities, as exhibited by humans in particular problem contexts, be used as a model and benchmark for the development of co…
